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Oregon Health & Science University, Department of Biomedical Engineering, Portland, Oregon 97239
University of Wisconsin, Department of Biomedical Engineering, Madison, Wisconsin 53706
Oregon Health & Science University, Heart Research Center, Portland, Oregon 97239
University of Washington, Department of Bioengineering, Seattle, Washington 98195
Recent advances in optical coherence tomography (OCT), and the development of image reconstruction algorithms, enabled four-dimensional (4-D) (three-dimensional imaging over time) imaging of the embryonic heart. To further analyze and quantify the dynamics of cardiac beating, segmentation procedures that can extract the shape of the heart and its motion are needed. Most previous studies analyzed cardiac image sequences using manually extracted shapes and measurements. However, this is time consuming and subject to inter-operator variability. Automated or semi-automated analyses of 4-D cardiac OCT images, although very desirable, are also extremely challenging. This work proposes a robust algorithm to semi automatically detect and track cardiac tissue layers from 4-D OCT images of early (tubular) embryonic hearts. Our algorithm uses a two-dimensional (2-D) deformable double-line model (DLM) to detect target cardiac tissues. The detection algorithm uses a maximum-likelihood estimator and was successfully applied to 4-D in vivo OCT images of the heart outflow tract of day three chicken embryos. The extracted shapes captured the dynamics of the chick embryonic heart outflow tract wall, enabling further analysis of cardiac motion.
This work is motivated by the need to analyze the motion of the embryonic heart and to elucidate relationships between altered cardiac mechanics during early development and congenital heart defects. Blood flow is essential for cardiac development, and alterations in blood flow dynamics can lead to heart defects,1 which affect about 1% of newborn babies. The interaction between cardiac tissue and blood flow dynamics determines the mechanical stimuli (e.g., stresses/strains) to which cardiac cells are subjected, stimuli that modulate cardiac growth and development. Images of the beating heart can then be used not only to visualize cardiac motion but also to quantify mechanical stimuli that modulate intrinsic cardiac genetic programs.
Optical coherence tomography (OCT) has been extensively used to study embryonic and cardiac development.2,3 Recently, the development of spectral domain OCT (SDOCT)4 made it possible to acquire in vivo image sequences of the developing heart5,6 and to obtain four-dimensional (4-D) [three-dimensional (3-D) images over time] OCT images of the beating embryo heart7,8 and blood flow velocities.9,10 Although OCT allows micro-structural and Doppler velocity visualization, quantification and analyses of cardiac motion and blood flow have been hampered by a lack of reliable, automatic procedures to extract the shape and motion of the developing heart from OCT images.
To analyze cardiac motion from in vivo OCT images, most previous studies used manual segmentation and manually extracted measurements.10,11 However, thorough manual analysis of OCT cardiac image sequences, which comprise large volumes of data, can be very time consuming and subject to operator errors. Therefore, to facilitate quantification and comparison of image datasets there is a dire need to develop automated analysis of cardiac motion from OCT images. Here, we present a semi-automatic, robust procedure to extract the dynamic shapes of the beating embryonic heart from 4-D OCT images.
We applied our segmentation procedure to 4-D images of a cardiac segment, the heart outflow tract (OFT), of chick embryos at the Hamburger-Hamilton12 developmental stage 18 (HH18), approximately 3 days of incubation. At HH18, the heart OFT has a tubular structure, with an external myocardial layer, an endocardial layer that is in contact with blood, and a cardiac jelly layer in between the myocardium and endocardium (Fig. 1). From OCT images, the myocardium, cardiac jelly and the blood can be readily distinguished. The endocardium that lines the lumen, however, cannot be distinguished from blood. This is, however, not an important limitation as the endocardium is a thin monolayer of endocardial cells and thus can be assumed to coincide with the lumen-tissue interface. During the cardiac cycle, the OFT myocardium contracts and expands approximately preserving a tubular shape. In contrast, upon contraction, the shape of the endocardium becomes irregular as it "folds" along lines that are approximately aligned with the direction of flow.11 Our developed procedure successfully detects and segments the OFT myocardium and endocardium over the cardiac cycle.
Structure of the heart outflow tract (OFT). (a) 3-D image set acquired with OCT, showing a longitudinal OFT section. (b) Sketch of the OFT showing its wall layers, as well as proximal and distal connections. The arrow shows the direction of blood flow. (c) and (d) Cross-sectional images at the inlet of the OFT during maximal contraction and maximal expansion. (e) and (f) Cross-sectional images at the outlet of the OFT during maximal contraction and maximal expansion. M: myocardium; C: cardiac jelly; L: lumen. Scale bars: 100 μm.
Automatic extraction of myocardium and endocardium shapes from 4-D OCT images presents several difficulties, including:
To overcome these difficulties, a segmentation procedure must distinguish target tissue from adjacent tissue, even as intensity lowers with tissue depth and must successfully track the large and irregular displacements of target tissues.
Algorithms have been developed to automatically extract the shape of tubular structures from vascular images.13–19 In particular, deformable models,20,21 such as active-contour (snake), have been widely used to detect the boundary of target tissues and extract their shapes.22–25 These models, however, typically fail to correctly detect target tissues when adjacent tissues with similar intensity are present. To correctly segment target tissue layers, piece-wise deformable models (a type of template-matching model) adjust their shape to match the shape of the tissue layer by using intensity values from inside and outside of the model.26–28 Here, we designed a robust two-dimensional (2-D) piece-wise deformable model, the double-line model (DLM), to locally detect the myocardial layer and the endocardial layer from 2-D cross-sectional images of the developing heart. A robust likelihood estimator is used to achieve accurate detection of the target tissue layer even with weakening intensities of OCT signals (lower signal-to-noise ratios) with tissue depth, and in the presence of adjacent tissues. Tracking of tissue layers was performed in space and time to achieve 4-D segmentations. The extracted cardiac shapes accurately captured the geometrical configuration of the OFT tissue layers and their motion over the cardiac cycle.
In this section we briefly describe the acquisition of in vivo imaging data using OCT, and image data reconstruction into 4-D images. Prior to imaging, white leghorn eggs were incubated to HH18 (∼72 h). The eggs were then removed from the incubator and placed in an organic glass cube in which temperature was controlled to approximately 37.5°C to keep the cardiac cycle at a physiological range (around 370 ms). The egg shell and part of the membrane overlaying the embryo were removed to provide optical access to the chick heart. A spectral domain OCT system was then used to image the OFT of the embryonic chick heart.8,9 The system acquired 256×512 pixel images (256 A-scans) at a rate of 140 frames per second (fps), with axial and lateral resolutions of ∼10 μm and ∼16 μm. Following our previously described 4-D imaging strategy,8 we acquired 2-D image sequences that included about four cardiac cycles (200 frames at speed of 140 fps) at different image planes along the OFT, separated by 7.5 μm. Our imaging procedure resulted in 120 frame sequences along the OFT (see Fig. 2). We also imaged longitudinal frame sequences (for reconstruction purposes). Raw image acquisition took about 20 minutes.
Schematic representation of OCT imaging strategy and reconstruction procedure. Cross-sectional OCT images were obtained by scanning the heart at 120 positions (separated by 7.5 μm) along the OFT. At each position, 200 sequential 2-D scans were obtained at 140 fps (about four cardiac cycles). A transverse position was also imaged (not shown). Acquired image sequences, however, start at different phases (timings) over the cardiac cycle. To synchronize the images, cross-sectional sequences are first pooled to one cardiac cycle, phases between image sequences are calculated, and then image sequences are interpolated so that sequences from all positions start at the same time and have the same number of image frames (196 frames over the cardiac cycle). 3-D images that show the position of the OFT over the cardiac cycle are then constructed to obtain 196 sets of 3-D images, with each 3-D image consisting of 110 positions along the OFT. t in the right image is time, and T is the period of the cardiac cycle (370 ms).
To obtain 4-D images of the OFT, the acquired OCT image frames were combined using our developed reconstruction procedure.8 Since image acquisition was not gated, we used similarity of lines from neighboring images to synchronize frame sequences acquired at different image planes along the OFT, and then we calibrated the synchronization using time-point lags estimated from longitudinal image frames. To generate the 4-D image dataset, we pooled the acquired image frames to one cardiac cycle interpolating the images to obtain several 3-D reconstructions over the cardiac cycle (see Fig. 2). Each reconstructed 4-D image dataset consisted of 3-D image datasets at 196 different time points (over one cardiac cycle), with each 3-D image dataset consisting of 180×265×110 voxels. We applied our shape extraction algorithm to these reconstructed 4-D OFT image datasets.
We designed an algorithm to extract the dynamic shapes of the heart myocardium and endocardium from reconstructed 4-D OCT images. We extracted three surfaces over time: the external and internal surfaces of the myocardium layer and the endocardium (or tissue-lumen interface) surface. Here, we first present a brief overview of the extraction procedure. Then, we explain in more detail the DLM model and the segmentation strategy employed.
To extract the shape of the cardiac tissue layers, we used 2-D cross-sectional planes swept along the centerline of the heart OFT (see Fig. 3 and Sec. 3.4 for a detailed description of how the centerline and cross-sectional planes are obtained), and a robust 2-D segmentation algorithm. The advantages of using 2-D cross-sectional planes are:
The 2-D segmentation algorithm was successively applied to 2-D cross-sectional planes over space and time to render 4-D segmentations. We used this strategy, together with smoothing and regularization procedures, to extract the shapes of the myocardium and endocardium over the cardiac cycle.
Sketch showing the OFT shape extraction procedure from OCT images. (a) Cross-sectional plane depicting the myocardium and endocardium layers, the myocardium mid-layer contour, a layer DLM placed on the myocardium and an edge DLM placed on the endocardium. (b) Initial cross-sectional plane. (c) Extraction of the cardiac shape is performed by sweeping cross-sectional planes along the OFT.
The 2-D segmentation algorithm was based on a local, piece-wise deformable double-line model [DLM; see Fig. 3(a)]. Two slightly different DLM formulations were developed: the layer DLM, and the edge DLM. To detect the myocardium layer (internal and external surfaces) we used the layer DLM, and to capture the endocardium layer (essentially a thin interface between the tissue and the lumen), we used the edge DLM [see Fig. 3(a)]. In our algorithm implementation, to detect cardiac tissues, several layer DLMs are placed around the myocardium and several edge DLMs are placed around the endocardium. The DLMs on each tissue layer are then linked together using active-contour techniques, and detection of the myocardial and endocardial layers is guided by a robust maximum-likelihood estimator. Linking the DLMs with active-contour facilitates detection and tracking of tissue motion.
Our extraction procedure segments the myocardium layer and then the endocardium layer (see Fig. 4). Extraction of 3-D surface shapes from the first 3-D image volume dataset (time t=0) starts with a manual setting of the myocardium mid-layer contour [a contour that divides the thickness of the myocardium layer in half, see Fig. 3(a)] on an initial plane. Layer DLMs are arranged around the mid-layer contour, and linked with an active-contour model. Then, guided by a maximum-likelihood estimator, the layer DLM thickness and position are adjusted to match the myocardium layer [see Fig. 3(b)]. The procedure is repeated for each cross-sectional plane until the entire 3-D volume data is spanned [see Fig. 3(c)]. To segment the myocardium at the next time-step t=Δt, the configuration of the layer DLMs at t=0 is copied to the 3-D volume data corresponding to t=Δt, and the myocardium layer is detected in the new time step. The procedure is repeated for all time steps, until the entire cardiac cycle is spanned. Smoothing in space and time is then performed to yield the myocardial shape.
Steps to extract the 4-D OFT myocardial and endocardial layer surfaces.
Once the myocardium has been segmented, we used the segmentation to mask the regions of the cross-sectional images that lie outside the inner myocardial surface (see Fig. 4). This is done to facilitate detection of the endocardium. To reduce image noise, we then generate a binary image that increases lumen contrast. Edge DLMs are then placed around the lumen-tissue interface, and linked with an active-contour. The position of the edge DLMs is then adjusted to match the endocardium layer on the preprocessed image. This is repeated in space and time until the endocardium from the whole 4-D dataset is segmented. Smoothing of the lumen/endocardium surface is then performed in space and time.
The layer DLM is a local 2-D template deformable model that we designed to track the myocardium layer, and is key to our myocardial shape extraction procedure. In this section, we will introduce the layer DLM, its main parameters, and the maximum-likelihood estimator used to match the DLM to the myocardial layer. We will also describe how DLMs are linked together with an active-contour model to smooth the contour and guide myocardium detection.
The DLM consists of two parallel lines: AB and CD [Fig. 5(a)], which are the outer and inner DLM edges, respectively. Basic parameters of the DLM describe its size and location: width wm; length lm; position pm (DLM center point position vector); and direction tm (a unit vector parallel to the DLM edges). These parameters also define a binormal vector nm, a unit vector perpendicular to tm and pointing from the DLM center to the DLM outer edge. M={wm,lm,pm,tm} represents the state of the DLM. To detect the OFT myocardium, layer DLMs are scaled by changing wm and lm; rotated by adjusting tm; and shifted by changing pm.
The layer double-line model (layer DLM). The layer DLM consists of two parallel lines AB (outer edge) and CD (inner edge), which can be adjusted to detect a tissue layer. (a) DLM parameters that define the DLM state : width, wm; length, lm; position, pm; and direction, tm. (b) Regions of the layer DLM: the DLM is divided into an internal region, Rmi, which is in turn subdivided into two subregions, Rmh and Rml; and two external regions, Rbo and Rbi. These regions are used to calculate foreground and background intensity levels from OCT images.
The layer DLM was designed to
Local DLM intensity values are used to determine adaptive intensity levels, which, within a maximum-likelihood estimator, guide adjustment of DLM parameters to match the myocardial layer locally.
To distinguish the target tissue from adjacent tissues, foreground and background intensity levels are estimated. The foreground intensity (If) is defined as the intensity of the target tissue; the background intensity (Ib) is defined as the intensity around the target tissue. In OCT images, the intensity at the center of the myocardial tissue layer is higher than the intensity near the tissue boundaries; also the intensity around the outer edge of the DLM may be different from the intensity around its inner edge. Thus, we define two levels of foreground intensity: high (Ifh), and low (Ifl); and two levels of background intensity: outer (Ibo), and inner (Ibi). To estimate If and Ib we use pixel intensities from regions inside and outside of the layer DLM, respectively [see Fig. 5(b)]. We divide the internal DLM region, Rmi, into two subregions, Rmh and Rml, corresponding to the middle portion of the DLM, and the portions adjacent to the edges, respectively [see Fig. 5(b)]. Thus Rml is a disjoint region. Similarly, we define two external regions, Rbo and Rbi, which are adjacent to the DLM outer and inner edges, respectively. The equations used to calculate foreground and background intensities are listed in Table 1. Foreground and background intensities are adaptive and depend on the local intensities of the region where the DLM is located. This allows successful detection of cardiac layers even when image intensity varies.
It has been shown that maximizing a likelihood estimator that uses intensity information from inside and outside of a deformable model can be successfully used to differentiate target tissues from adjacent tissues.17,26,29 Inspired by these studies, we define the normalized likelihood function Ln in discrete form by: Display Formula
To detect the target tissue, we find the DLM state M={wm,lm,pm,tm} that maximizes Ln(M|I). Because the likelihood function uses local intensity information from inside and outside of the DLM, the detection algorithm is robust and can locally find the target tissue even when the local image is noisy and intensity is low.
The layer DLMs are used to detect the myocardium locally. To mitigate local uncertainties, and facilitate smoothing, the DLMs placed around the myocardium layer (see Fig. 6) are linked with an active-contour (snake) model. The snake model is a classical method to detect the contour of an object in an image by means of energy minimization.22 The energy function, Esnake, of the active-contour model that we used here is, Display Formula
Localization of DLMs around the myocardial layer and lumen interface.
The layer DLM can be used to locally find the target tissue, by changing its size, position and direction to approach the tissue layer and, finally, match the tissue layer [see Fig. 7(a)–7(c)]. However, sometimes detection fails, especially in situations when there is: 1. close proximity of target and adjacent tissue layers [e.g., Fig. 7(i)]; 2. incorrect initial orientation of the layer DLM [e.g., Fig. 7(h)]; and 3. overlap of layer DLMs placed around the myocardium layer. To avoid these issues and increase the robustness of the extraction procedure, we constrained the layer DLM state parameters. Constraint values were found empirically from images, but we could use them in several image datasets without change.
Detection of the myocardial layer (target tissue) from OCT images of the embryonic heart OFT using the layer DLM. (a–c) Detection process. (d–g) Successful segmentation results: the layer DLM can successfully detect the myocardial layer even when there are adjacent tissues (in d and e) and intensity is weak (in f and g). (h) and (i) Examples of failed detection of the myocardium: adjacent and target tissues detected together (in i) and the orientation of the DLM is incorrect (in h). Scale bars: 100 μm.
To prevent simultaneous detection of both the target and adjacent tissue layers, we set bounds on the value of the DLM width wm. The maximum wm was constrained to be larger than the actual thickness of the target tissue layer, but smaller than about twice the target layer thickness. From analysis of OCT cross-sectional images, the thickness of the OFT myocardial layer was about 14 pixels (70 μm) near the OFT inlet, and 10 pixels (50 μm) near the OFT outlet. Thus, we set the maximum wm to be 16 pixels near the OFT inlet, and 14 pixels near the OFT outlet. We also set the minimum wm to be 4 pixels (20 μm) everywhere. To avoid incorrect orientation of layer DLMs, the DLM direction, tm, was aligned to the tangent of the active-contour curve that links the DLMs around the myocardium (see Fig. 6). Finally, to prevent overlapping of layer DLMs, DLM shifts were allowed only in the direction of the DLM binormal nm (that is perpendicular to the active-contour curve). Placing 40 layer DLMs around the myocardial layer in a cross-sectional image (see Fig. 6) was found to be enough to achieve an accurate segmentation of the myocardium.
Parameters of the active-contour model [see Eq. (2)] that linked the layer DLMs were also found empirically. Because the second term in Eq. (2), vss(s), is related to the contour curve smoothness, β was chosen to be larger than α and γ. Setting β about 1.5 times larger than α and γ (α=15, γ=15) worked well in implementation in several image datasets.
The procedure to extract the endocardium layer from the cross-sectional images is similar to the procedure used to extract the myocardium layer. In OCT images, the endocardium layer cannot be distinguished, but the lumen-wall interface appears as the "edge" between the low-intensity cardiac jelly and the high-intensity endocardium and lumen. However, the lumen edge is frequently blurred in OCT images and to facilitate detection, we preprocessed the images to increase contrast and reduce noise. In the preprocessed images, the lumen-wall edge shows as a region of finite width in which intensity changes continuously (from dark to bright), therefore suited for detection using a DLM. Changes to the DLM formulation to detect the lumen-wall interface (the "edge" DLM) included redefining the foreground and background intensities and the DLM constraints and changing the definition of the associated maximum-likelihood estimator.
To generate preprocessed OCT images, we used an adaptive intensity threshold on the previously identified 2-D cross-sectional images and created a binary image with intensities either set to a fix high value, Ih, or 0, with lumen areas showing with high-intensity. Because only the tissues inside the region delineated by the inner contour of the myocardium layer (the inner-layer contour, see Fig. 6) are useful for extracting the lumen-wall interface, the intensity of the pixels that are outside of the inner-layer contour are set to 0 (masked out) in the binary image. To increase image contrast, the intensity of the pixels within the inner-layer contour is calculated using the median intensity of a small image region (window) centered at each pixel. If the intensity of the pixel is smaller than the median intensity of the region, pixel intensity is set to 0; otherwise, it is set to Ih. To reduce noise, we then calculate the mean intensity of three binary images: the image of interest and the two images that are adjacent to it in a 3-D image dataset (see Fig. 8). This operation reduces the sharpness of the lumen "edge" allowing use of a formulation that employs DLMs. We then use this preprocessed image to extract the lumen-wall interface.
Image preprocessing for extracting the 2-D lumen contour. Scale bars: 100 μm.
To detect the lumen-wall interface, we slightly modified the formulation of the DLM, resulting in what we called an edge DLM formulation. The edge DLM is similar to the layer DLM, except that:
The maximum-likelihood estimator function used for detection of the lumen-wall interface, Le, is defined as follows:Display Formula
The edge DLMs on the lumen contour are connected by an active-contour model. Similar to how layer DLMs are used to detect the myocardium, in detecting the lumen-wall interface at each cross-sectional preprocessed image, the likelihood function Le is locally maximized.
The values of parameters needed to obtain an accurate segmentation of the lumen were determined empirically and tested on several image datasets. For the binary image, we used Ih=120. Further, to make the lumen visible and yet accurate for extraction, the size of the window used to calculate the binary intensity of pixels was set to 31 pixels (that is about 165 μm) in the image vertical and horizontal directions, centered on the pixel of interest. These values resulted in preprocessed images that accurately showed the lumen domain (see Fig. 8).
To detect the lumen-wall interface, we set wm to be 4 pixels (20 μm), and background and foreground intensities to be constant: Ibo=0, Ibi=120, If=120. Due to the irregular shape of the lumen contour, edge DLMs easily overlap when using an active-contour model to smooth the contour. Instead, we smooth the contour by averaging the position of adjacent DLMs on the lumen contour and, like for the layer DLM, use the active-contour to constrain edge DLM orientation and shifts. We found that placing 80 edge DLMs around the lumen contour in a cross-sectional plane was sufficient to accurately extract the lumen-wall interface.
Extracting 4-D OFT shapes is performed as summarized in Sec. 3.1. We first determine the orientation of cross-sectional planes along the OFT, and extract the contours of the myocardial layer from cross-sectional images, using layer DLMs linked by active-contour models (see Fig. 4). Then, the lumen contours are extracted using edge DLMs applied to preprocessed cross-sectional images. It is worth noting here that we have not implemented a global optimization function on our procedure. Instead, our approach is to find an overall solution by successively solving local optimization problems:
Step 2 changes the position of DLMs, and thus the procedure is iterated to find accurate solutions. Further, smoothing in space (outside of the plane) and time is also performed, and the successive procedure described above applied again after smoothing.
In this section, we describe in detail how we determine the orientation of cross-sectional planes, how the iterative segmentation procedure is applied, and how we smooth the extracted contours in space and time.
A cross-sectional plane is defined here as a plane approximately perpendicular to the 3-D centerline of the OFT myocardium, and is represented by a position vector, ps, and a normal vector, ns (see Fig. 3). Changing ps shifts the plane, and changing ns rotates the plane. In our implementation, ps is the centroid of the myocardium mid-layer contour. Identification of cross-sectional planes in 4-D starts with a manual setting of the myocardium mid-layer contour on an initial plane on the first 3-D image volume dataset (t=0). Layer DLMs are arranged around the mid-layer contour, linked with an active-contour model, and used to detect the myocardium layer on the plane. The initial plane chosen, however, is not necessarily a cross-sectional plane. To find a cross-sectional plane, the initial plane is rotated. DLMs are used to match the myocardium layer after each rotation, until a plane that has the minimum mid-layer contour perimeter (the cross-sectional plane) is found. The DLM configuration is then copied to a new plane that is parallel to the cross-sectional plane found but slightly shifted in the direction of the plane normal. DLMs then are used to match the myocardium layer, and the plane is rotated until a new cross-sectional plane is found. Plane shifting and rotation are repeated until the entire OFT from the first 3-D volume data is spanned [see Fig. 3(c)]. The centroids of the cross-section mid-layer contours are then extracted to form the OFT centerline, which is smoothed in space. After centerline smoothing, cross-sectional planes are adjusted, so that they remain perpendicular to the centerline and detection of the myocardium layer is performed on adjusted planes. This procedure results in an initial segmentation of the myocardium layer at time t=0.
To segment the myocardium at the next time-step, t=Δt, the configuration of the layer DLMs at t=0 is copied to the 3-D volume data corresponding to t=Δt, and the segmentation procedure is used to change the DLM thickness and position to match the myocardial layer in the new volume dataset. The OFT centerline is then extracted, smoothed in space, and cross-sectional planes are adjusted. This is followed by detection of the myocardial layer on the adjusted cross-sectional planes. The procedure is then repeated for all time steps until the entire cardiac cycle is spanned. This strategy provides a good initial estimation of the position and orientation of cross-sectional planes in 4-D.
After the initial estimation of cross-sectional planes described above, the location and orientation of the planes is adjusted once more. To avoid spurious oscillations near the boundaries (the inlet and outlet of the outflow tract), the first and last cross-sectional planes along the OFT are fixed in space (ps and ns do not change over time). For these planes, ps and ns are the mean of the previously estimated normal and position vectors over time. Plane positions in between the first and last planes are adjusted by first smoothing the centerlines in space and time. Smoothing in space and time uses data from adjacent planes: suppose that Sij denotes a quantity to smooth on the i'th cross-sectional plane at time j; the new, smoothed value of Sij is then caculated as the mean of S(i−1)j, S(i+1)j, Si(j−1), and Si(j+1). This procedure is performed for each component of ns and ps, and for each plane (except the first and last), and each time-point. Cross-sectional planes are then arranged so that distance within planes is as uniform as possible, while avoiding cross-sectional planes from crossing each other (such that a nonoverlapping mesh can be constructed directly from extracted results). In regions where the OFT centerline is rather straight, plane positions are approximately equidistant along the centerline, and in regions where the radius of curvature of the OFT centerline is small, distance between planes is larger (proportional to the contour mean radius and the angular change of ns). This last step determines the final position and orientation of cross-sectional planes in 4-D.
Inaccurate segmentation could happen in our procedure because, like with other deformable models, layer detection using DLMs depends on the DLM initial state. Even after combining DLMs with active-contour models, and setting constraints for the DLM parameters (thickness, orientation), we still found a few sections that were inaccurately segmented (especially close to the OFT outlet, where signal-to-noise ratios diminish due to tissue depth). To solve this problem, an interactive interface was designed to allow the user to manually adjust cross-sectional contours and thus reset the state of the DLMs when needed. With this procedure, accurate segmentation of the 4-D cardiac OFT wall layer was achieved. In our implementation, inaccurate segmentations occurred at a very low frequency, with at most 0.2% frames showing failed segmentations. Nevertheless, even after detection failed, the DLM accurately found the target tissue again at the next adjacent section or time step.
Myocardium layer detection presents local fluctuations in space and time that need to be further smoothed. We proceed by first smoothing the myocardium mid-layer surface, which is described by the position vectors, pm, of the layer DLMs. Smoothing in space and time was performed (as in Sec. 3.4.1) by calculating the mean position of four spatiotemporally adjacent layer DLMs: the corresponding positions of two adjacent cross-sectional planes of the 3-D image set and the corresponding positions of two adjacent time points (in plane smoothing was performed using the active-contour). After smoothing the myocardium mid-layer, layer DLMs are used once more to accurately find the myocardium thickness at the new mid-layer positions. Outer and inner surface layers of the myocardium are then obtained by linking the outer and inner edges of the layer DLMs (see Fig. 6). Smoothing of the outer-layer and inner-layer surfaces is also carried out by calculating the mean positions of the four spatiotemporally adjacent points on the surfaces.
Once the myocardium layer is extracted, we proceed to extract endocardium/lumen surfaces. To this end, we first generate preprocessed images of each cross-sectional plane as described in Sec. 3.3.1. For the very first cross-sectional plane, edge DLMs are placed manually around the lumen edge on the preprocessed image, and the DLM parameters are optimized by maximizing Le, Eq. (3). Edge DLM parameters are then copied to the adjacent cross-sectional plane and then optimized. This is repeated until the whole 3-D dataset is segmented. For subsequent 3-D datasets, edge DLM parameters are copied from the previous time step, and then optimized. Like in the case of myocardial surfaces, after obtaining the lumen surfaces, surface smoothing is performed by calculating mean positions of four spatiotemporally adjacent edge DLM positions. This step completes the extraction procedure of 4-D surfaces from 4-D OCT images of the chicken heart outflow tract.
We semi-automatically extracted the shapes of HH18 OFT cardiac tissue layers from 4-D in vivo OCT images of the chicken heart. More precisely, we extracted 4-D surfaces of the myocardium and tissue-lumen interface over 196 time points spanning one cardiac cycle (approximately 370 ms), with each 3-D OFT shape consisting of 50 cross-sections spanning around 700 μm along the OFT centerline. While we analyze here the extraction from one set of 4-D images in detail, we have so far successfully extracted data from seven HH18 OFTs.
To evaluate the accuracy of the extracted results, we compared segmentations of 2-D cross-sectional images performed with our extraction algorithm and manually. For this study, manual segmentation was performed at three positions along the OFT: near the inlet, approximately in the middle, and near the outlet; at three different time points during the cardiac cycle: when the OFT walls are closed, half-closed, or opened (see Fig. 9, first to third rows). To compare results we used the areas enclosed by contours obtained by manual segmentation, Ama; contours obtained by automatic segmentation, Aau; and Aco, which is the area of the intersection between the automatic and manually extracted regions. Two metrics, recall and precision, were used to evaluate the accuracy of the automatic segmentation. The recall, defined as Aco/Ama, measures the ability of the automatic extracted result to match the target tissue; the precision, defined as Aco/Aau, measures the ability of the automatic extracted result to contain only the target tissue. The average recall of the myocardium from the nine selected 2-D cross-sectional images was 84.7%, with a standard deviation of 3.3%, and its precision was 92.8%, with a standard deviation of 4.4%. The average recall of the lumen from the nine selected cross-sectional images was 91.9%, with a standard deviation of 3.3%, and its precision was 93.2%, with a standard deviation of 5.1%. To further confirm the overall accuracy of the segmentation, we visually inspected automatic segmentation results on OFT longitudinal sections at three different time points (see last row in Fig. 9). We found that in most places deviations between visual estimations of the location of a boundary and segmentation lines were smaller than 1 pixel (around 5 μm). The comparison of segmentation results on the 2-D cross-sections and longitudinal sections demonstrated that our segmentation strategy can accurately extract the OFT surfaces of the myocardium and lumen from 4-D OCT images of the developing heart even when images are noisy and intensity levels (as well as signal-to-noise ratio) diminish with depth.
Automatic extracted myocardial layer and lumen surfaces. (First to third rows) Segmentations of cross-sectional planes perpendicular to the 3-D centerline of the OFT at three time points over the cardiac cycle. (Bottom row) Segmentations of OFT longitudinal sections. Scale bars: 100 μm.
Figure 10 shows extracted OFT surfaces from 4-D OCT images (myocardial surfaces and the lumen-tissue interface surface) as well as volume data (see also Videos 1, 2, and 3). The extracted OFT shapes showed the peristaltic-like motion of the myocardial and endocardial layers of the OFT over the cardiac cycle, characterized by a contraction wave that travels through the OFT. During contraction, the OFT lumen appears to close, preventing blood flow, and folding of the endocardium layer becomes apparent. Folding and unfolding of the endocardium layer are observed from the extracted endocardium surfaces (as well as in raw images). The extracted shapes are therefore useful not only to visualize cardiac motion over the cardiac cycle but also in the analysis of cardiac motion.
Extracted 4-D OFT shapes over one cardiac cycle. (a) Extracted OFT layers (represented as surface meshes) versus the original image at time 0 ms; see also (Video 1, MOV, 7.96 MB) [URL: http://dx.doi.org/10.1117/1.JBO.17.9.096005.1] showing the steps of the segmentation procedure. (b) and (c) Extracted results at times 95 ms, 190 ms, 285 ms, and 370 ms (cardiac cycle is 370 ms). Due to the periodic motion of the heart, the shape of the OFT at time 370 ms was the same as at time 0 ms. (b) OFT myocardial and endocardial surfaces; see also (Video 2, MOV, 9.30 MB) [URL: http://dx.doi.org/10.1117/1.JBO.17.9.096005.2] showing the motion of extracted surfaces (green: outer myocardium surface; blue: inner myocardium surface; red: endocardium/lumen surface). (c) 3-D volume data images of the OFT; see (Video 3, MOV, 7.67 MB) [URL: http://dx.doi.org/10.1117/1.JBO.17.9.096005.3] showing the motion of the heart from the extracted volume data (red: lumen; blue: myocardium, showing intensity levels in shades of blue).
We successfully extracted OFT shapes from six other 4-D image datasets of embryonic chick OFTs at HH18 (not shown). These chick embryo heart OFTs had similar size, but 4-D OCT images differed in intensity and level of noise. In segmenting the OFTs from these datasets, and due to the robustness of our extraction procedure, there was no need to adjust DLM parameters in our algorithm. However, a parameter of the active-contour model, β [see Eq. (2)] was adjusted on a case-by-case basis to avoid deviations of the active-contour from the target tissue layer. The range of β values used was from about 1.3 to 1.9 times the value of the other two parameters α and γ (α=γ=15), which were not modified. Larger values of β were used when adjacent tissues outside of the myocardium had higher intensity levels than the myocardium tissue because this choice of β penalized deviations from the myocardium layer by reducing curvature changes and increasing smoothing. For other cases, the smaller values of β were chosen. All seven extracted 4-D chick heart shapes showed similar peristaltic-like motion of the myocardial and endocardial layers of the OFT, and endocardial folding upon contraction.
In this paper, we present a semi-automatic procedure to extract the shape and motion of developing heart tissues from 4-D OCT images. The extracted tissue layers included the myocardium (internal and external surfaces) and the endocardium (which also represents the lumen-tissue interface). While the method has been applied to 4-D OCT images of the heart outflow tract of HH18 chick embryos, the procedure could also be applied to other stages of development, other portions of the heart, and also to other animal models and images acquired with different imaging modalities. This is, however, outside the scope of this work. The main characteristic of the presented procedure is that it can accurately track the dynamics of tissue layer motion over time, even when motion and deformation are large, and in the presence of image noise, gaps, and variations in intensity.
The main drawbacks of the extraction procedure presented are that the procedure is sensitive to initial conditions, and it has a relatively high computational cost. Like with other deformable models, procedures to match the layer and edge DLMs to tissue layers are sensitive to the initial state (e.g., position, orientation). To increase the robustness of the procedure, we linked DLMs with active-contour models and constrained some of its parameters (thickness, orientation). To increase the accuracy of the endocardium detection, which presents "folding" patterns, in our procedure we preprocessed cross-sectional images. Nevertheless, we found a few sections that were inaccurately segmented (<0.2%). We solved this problem by designing an interactive interface that allows the user to first check the accuracy of the segmentations, and, when needed, manually correct segmentations that are not accurate. Manual correction allows "resetting" the state of DLMs, improving the initial conditions and facilitating accurate detection.
Our segmentation strategy differs from other methods to extract tissue layers. Most methods13,14 detect tissue layers using intensity variations, but detection becomes difficult when adjacent tissue layers are present. Unlike other template-matching deformable models used to detect tissue layers,20 which only consider intensities inside of the model, our procedure uses intensities from inside and outside of the DLM; further intensity levels are adaptive to adjust for the diminishing intensities with tissue depth.
Robustness of the procedure was achieved at the cost of a modest increase in computations. To achieve robustness, the layer DLM uses adaptive foreground and background intensity levels. Further, layer DLMs are linked with an active-contour model. Nevertheless, in our system (Dell T7400 workstation with eight core CPUs at 2.0 GHz and 10.0 GB RAM), extracting the OFT cardiac shape and motion from 4-D OCT images took about 3 h. Comparing this performance to that of manual segmentations, which can take more than a week for the whole 4-D image dataset, the proposed procedure enables faster and accurate analysis of cardiac motion. Thus, while computationally expensive, 4-D segmentation of the OFT of HH18 chick embryos is feasible.
The main advantage of our proposed semi-automatic procedure, is that it achieves robust segmentation of cardiac tissues from highly dynamic 4-D OCT images. This was confirmed by applying the algorithm to several 4-D OCT image reconstructions of the chick heart OFT, images that differ in the level of noise, image intensity and embryo orientation, and were also affected by biological variations, and possibly inaccuracies in embryo staging. The presented extraction procedure successfully segmentated all datasets. We are confident that we could also successfully apply the algorithm to other developmental stages and sections of the heart, enabling the study of changes in cardiac motion over developmental stages and after interventions.
The extracted 4-D results provide detailed information on the motion of the OFT wall, which could be used to quantify motion, tissue strains, or as input for subject-specific computational models of the developing heart OFT, among other applications. This work then provides a foundation to later work that aims at understanding how biomechanical stimuli affect cardiac development.
This work has been supported in part by Grants NIH R01 HL094570 and NSF DBI-1052688. The content is solely the responsibility of the authors and does not necessarily represent the official views of grant-giving bodies. The authors would like to thank Dr. Zhenhe Ma (Northeastern University at Qinhuangdao) for setting up the OCT system employed; and Dr. Cindy Grimm (Washington University in St. Louis) for carefully reviewing this paper and providing insightful suggestions.
Xin Yin ; Aiping Liu ; Kent L. Thornburg ; Ruikang K. Wang and Sandra Rugonyi"Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images", J. Biomed. Opt. 17(9), 096005 (Sep 13, 2012). ; http://dx.doi.org/10.1117/1.JBO.17.9.096005
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|Amido derivatives of 2,4,6-triiodobenzoic acids containing at least one N-hydroxyalkyl and at least two hydroxyl groups
|This invention relates to novel X-ray contrast agents particularly for use in the cerebrospinal cavities, comprising non-ionic alkanols carrying at least one N-bonded secondary or tertiary amide group and having at least two hydroxyl groups and at least one iodine atom in the molecule. Particularly useful compounds include the N-hydroxyalkyl iodoalkane sulphonamides having at least two hydroxyl groups, tri- and tetra-iodobenzene carrying carbamoyl, acylamino and/or acylaminomethyl substituents and having at least two hydroxyl groups in the molecule and at least one N-hydroxyalkyl group. Particularly preferred compounds comprise 2,4,6-triiodobenzamides which may be 3- and/or 5-substituted with a variety of groups. The compounds all show markedly low toxicities and a number show very high levels of water solubility.
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This question comes up a lot. What wears a spring out is the act of compressing and uncompressing. Thus a mag loaded is not wearing out the spring.
If you do the physic calculations, you can probably make a case that a spring under tension wears more quickly than an unloaded one, but the difference is so little as to be immaterial. If you ever need a mag in an emergency, you'll probably not have the luxury of being able to load it.
I keep all mine fully loaded.


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BANGKOK (dpa) – Apple Inc (EPA pic, below) will include the Beats music subscription service with its operating system from early next year, a media report said.
The move will make the service available on hundreds of millions of Apple mobile phones and tablets, the Financial Times reported Wednesday.
Apple bought the music streaming service and headphone manufacturer this year for three billion dollars. An update to the iOS software could come as early as March, the report said, citing people familiar with the situation.
Pre-installed apps are seen as a short-cut for customers. Apple has around 200 million active customers of its iTunes download service, according to Midia Research. It estimated that Beats has around 110,000 subscribers to its service, the report said. Apple will likely rebrand the Beats service and run it on a paid subscription model, the Financial Times said. Music streamer and Beats competitor Spotify has around 10 million subscribers to its service.


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This year, as Bowie's Boys and Girls Club celebrates its 50th anniversary, organization leaders say they are losing members. Parents are putting their children in other competitive sports leagues that travel beyond the city's borders, club leaders said.
"We're not geared towards going to national competitions in Florida," said Joe Hoyt, president of the Bowie Boys and Girls Club. "We are getting picked and pulled on from other organizations."
The nonprofit organization — which provides athletic opportunities for youths between 4 and 18 years old in a wide range of sports — first started in Bowie in 1963. At its height, around the late 1980s, the local club had about 3,000 youths, but membership has declined to about 2,500, club officials said. The bulk of the club's activities are intramural games that pit teams of Bowie children against one another.
In the face of competition from leagues with more opportunities for travel and exposure, the club can't do much without turning away from its locally focused mission, Hoyt said.
Despite the decline, the club has managed to keep revenue steady by increasing registration fees from about $95 in 2003 to about $175 today, Hoyt said. That price can vary based on factors such as how many sports a child signs up to play, how many children a family has signed up and whether there is a need for financial assistance, according to the club. Fees bring in 95 percent of the organization's annual revenue, which tends to be about $240,000, with the rest coming from fundraising, Hoyt said.
The club's cheerleading commissioner, Joy Holmes, said she can't see the club going away because children need a place to get basic skills.
"You have to start somewhere; you have to have a foundation," she said. "The Boys and Girls Club has such a positive history in the community and longevity in the community. We're here to stay."
Competitive tournaments and programs shouldn't be viewed as competitive or in opposition to locally focused clubs, said Sean Kolb, a tournament director for the United States Specialty Sports Association.
"In sports, there's different levels of talent and of desire. If you can provide different levels [of competitiveness], it's a service to your community," said Kolb, who also is softball commissioner for the Olney Boys and Girls Community, a group not affiliated with the Boys and Girls Club. "I don't look at it as one versus the other. That doesn't help anyone."
Megan McDonald, an 11-year-old from Olney, has played on the Olney club's travel team in tournaments such as those run by USSSA. Playing for a travel team geared toward tournaments has meant she practices about four times a week for 90 minutes at a time.
"When I was on [intramural] house [team], we didn't have any practices, just games. Now, we have practice all the time," said Megan, a fifth-grader at Greenwood Elementary School. "I'm getting a lot better."
Bowie Boys and Girls Club is working to develop young athletes as good role models with new community service projects for the group, Hoyt said. In September, the club adopted a portion of Route 197, near Race Track Road, which athletes will help clean in the spring. Hoyt wants to get more youths involved in supporting the Bowie Interfaith Food Pantry, which provides food assistance to families.
"We are evolving. We are trying to ensure that we keep the old standards in place while making it more interactive for kids today," Hoyt said.
The competitive leagues weren't a factor when Fran Stevens, 52, was growing up in Bowie. Stevens began playing with the club when he was 8 years old, shortly after his family moved to Bowie around 1968.
"If [a more a competitive league was] around, it wasn't that popular," he said. "Everything stayed in the local level; you wouldn't go all over the state or down to Virginia."
The challenge of organizations recruiting athletes has gotten to a point where it makes Stevens reconsider some scrimmages, he said.
"I, in general, won't scrimmage a team that can recruit my kids," said Stevens, the commissioner of the Bowie club's basketball program for 14- to 18-year-old youths. "I don't want their coaches or their parents talking to my kids, telling them they should come with them."
In its years of operation, the club has become a family affair for some, such as the Stevens family. Stevens has been coaching for the organization since about 1985 and has coached all four of his children. Stevens' eldest children also have become club coaches.
"They understand what it's like giving back and staying in the community," he said.


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- Bladder Cancer Slideshow
- Prostate Cancer Slideshow Pictures
- Cervical Cancer Slideshow Pictures
- Patient Comments: Bladder Cancer - Effective Treatment
- Patient Comments: Bladder Cancer - Share Your Experience
- Patient Comments: Bladder Cancer - Symptoms
- Patient Comments: Bladder Cancer - Diagnosis
- Patient Comments: Bladder Cancer - Transurethral Surgery Experience
- Patient Comments: Bladder Cancer - Chemotherapy Experience
- Find a local Oncologist in your town
- Bladder cancer facts
- What is the bladder?
- What are the layers of the bladder?
- What is bladder cancer?
- What is the burden of bladder cancer in the U.S.?
- What are the types of bladder cancer?
- What are bladder cancer causes and risk factors?
- What are bladder cancer symptoms and signs?
- How is bladder cancer diagnosed?
- How is bladder cancer staging determined?
- What is bladder cancer grading?
- What is transurethral surgery (TURBT) for bladder cancer?
- What is the treatment for superficial bladder cancer?
- What is surveillance for bladder cancer?
- What is the treatment for muscle-invasive bladder cancer?
- What is chemotherapy for bladder cancer?
- What is the prognosis for bladder cancer?
- Can bladder cancer be prevented?
- Where can people find more information on bladder cancer?
- What research is being done on bladder cancer?
Quick GuideBladder Cancer Symptoms, Stages, Treatments
What is surveillance for bladder cancer?
Patients diagnosed and treated for superficial bladder cancer need regular follow-up to detect recurrences and treat them effectively. The following is a typical follow-up protocol:
- Cystoscopy and urine cytology every three months for two years, every six months for the next two to three years, and annually thereafter
- Imaging study (CT scan/intravenous urogram) of the kidneys and ureters once every year (especially for high-grade tumors/those associated with CIS)
Cystoscopy and cytology detect recurrence in the bladder itself while CT/IVU is used to detect a tumor in the kidneys and ureters. Patients with bladder cancer are more likely to get upper urinary tract (kidney and ureter) tumors that arise from the inner lining of these organs and share a common origin with bladder tumors. The risk of upper urinary tract recurrence depends on the stage and grade of the initial disease and the response of the tumor to BCG. Individuals with recurrent high-grade bladder tumors can have a risk of developing a tumor in the upper tracts and need to be followed closely in this regard.
Commercially available tumor markers that are used to test urine samples for evidence of bladder tumor recurrence are also being used in follow-up protocols. However, their exact role is undefined as of now, and they are not considered an adequate substitute for cystoscopy and cytology. Some of these tests and markers are NMP 22, BTA Stat, BTA Trak, and UroVysion.
What is the treatment for muscle-invasive bladder cancer?
Muscle invasive bladder cancer generally requires a more aggressive treatment plan than superficial bladder cancer. The standard and most effective treatment is the surgical removal of the bladder and diversion of the urinary stream using intestinal segments. This procedure, known as radical cystectomy, is a major operation and a thorough discussion and counseling of the risks, complications, and benefits is warranted prior to this surgical procedure.
In short, the procedure entails removal of the bladder, prostate, seminal vesicles, and the fatty tissue around the bladder through an incision made in the abdomen. The surgery also includes removal of lymph nodes in the pelvis on both sides of the bladder to detect their involvement with the cancer. This helps in deciding further management after surgery, including the need for chemotherapy. It has been shown that patients who undergo a thorough lymph node dissection have a better chance of cure as compared to patients who either do not receive one or undergo a less extensive dissection.
Radical cystectomy can be performed via open surgery, laparoscopy, or with robotic assistance. It has been shown that the outcomes in terms of cancer control and cure rates do not differ between these different approaches. However, the use of laparoscopic and robotic approaches significantly decreases blood loss during the surgery, decreases the need for blood transfusions, and may help in early recovery by decreasing postoperative pain at the surgical site. An additional advantage of the robot is that it permits an enhanced magnification of the surgical field with three-dimensional vision, which helps to enhance surgical precision. It is very important to note that all these approaches can achieve comparable results in terms of cancer control in the hands of surgeons skilled and experienced in a particular modality. So, the comfort factor and experience of an individual surgeon in a particular approach should play a major role in the patient's decision regarding selection of the approach for surgery.
Once the bladder has been removed, the urine needs to be diverted. There are three popular ways of doing that. All of them require the use of segments of the intestine that are still connected to their blood supply but have been disconnected from the gastrointestinal tract.
- Ileal conduit is the most extensively used form of urinary diversion. It is also the simplest and the least time-consuming form of diversion and has the least chances of complications in the short and long term. This entails the use of an intestinal segment, one end of which is connected inside the body to the ureters that drain urine into it from the kidneys, while the other end is brought out to the level of the skin and is covered by an external appliance (a "urostomy bag"). The urine from the kidneys continuously drains into the bag through the ureters and the "ileal conduit." The bag can be emptied at regular intervals or when it is nearly full by opening a tap-like attachment at the lower end of the bag. The major advantage of this procedure is that is relatively straightforward to perform with the least chances of complications. The disadvantages include the need to wear a bag all the time and the resultant negative impact that may occur on body image. This procedure is also termed incontinent diversion.
- Orthotopic neobladder entails the creation of a new bladder ("neobladder") with the help of an intestinal segment and connecting it to the natural urinary passage so that a person may be able to pass urine "more normally." The major advantage of this procedure is that it avoids the need to wear a bag, and the patient can pass urine in a more natural fashion. However, this is a more difficult procedure with a longer recovery period and may lead to some short-term and long-term complications, including persistent urinary leakage and inability to pass urine requiring the use of self-intermittent catheterization (passage of a tube into the urinary passage to empty the bladder). Some senior patients and those with certain medical conditions that affect the function of the kidneys or impair their ability to self-catheterize may be best served by the ileal conduit rather than the neobladder.
- Continent catheterizable pouch (for example, "Indiana pouch") is a form of neobladder which is not connected to the normal urinary passage but instead has an opening or a "stoma" at the level of the skin on the abdomen through which a catheter can be passed to empty it periodically. This has a valve-like mechanism that prevents the leakage of urine through this opening thereby precluding the need to wear a bag. It is used it patients desirous of and fit for a continent urinary system while being unsuitable for an orthotopic neobladder due to certain circumstances such as cancer at the point where the bladder joins the urethra. This procedure is also termed continent diversion.
Radical cystectomy (open, laparoscopic, or robot assisted) combined with one of the three urinary diversion methods is the gold standard for the treatment of muscle invasive and selected cases of high-grade superficial bladder cancer. Certain patients, however, may be unfit or unwilling to undergo this surgery. Segmental, or partial cystectomy is rarely done. Bladder cancer is so often multifocal in the bladder that such an approach is rarely effective. These patients can often undergo a combination of extensive TURBT, chemotherapy, and radiation in an attempt to cure or control the disease without the need to remove the urinary bladder surgically. Most experts believe that this regime may not be as effective as a radical cystectomy but can be used as an option in unfit/unwilling patients. Radiation to the bladder can, however, lead to its own set of problems and complications, including radiation damage to the bladder and rectum which give rise to bleeding and irritative symptoms ("radiation cystitis" and "radiation proctitis").


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Affiliations: Washington University School of Medicine, St. Louis, MO,
Mayo Clinic College of Medicine, Rochester, MN
Correspondence to: Stephen S. Lefrak, MD, FCCP, Professor of Medicine, Director, Humanities in Medicine Program, Washington University School of Medicine, Campus Box 8052, 660 South Euclid Ave, St. Louis, MO 63110-1093; e-mail: email@example.com
Dr. Mark Rosen has elucidated the deliberations of the American College of Chest Physicians (ACCP) in reviewing its decision in 1989 to bestow an award on Dr. Friedrich Wegener.1 The 1989 decision took place without the ACCP having the benefit of Dr. Wegener's complete curriculum vitae. We applaud the frankness and good will of the ACCP in engaging in this discussion. While we understand the dangers of the "retrospectoscope," we believe that the ACCP, in spite of its carefully considered judgment, should move to correct a misjudgment of the past and retract the award to Dr. Wegener. What has changed is not the values or mores of the ACCP, as strongly held in 1989 as today, but biographical facts are evident today that if known in 1989 would have disqualified Dr. Wegener as an ACCP awardee then, as they do now.
This discussion has been brought about by biographical information concerning Dr Wegener's professional life brought to light by one the authors2 and conveyed to the ACCP by the other author. Dr. Rosen's column details Dr. Wegener's National Socialist Party (Nazi) membership, his ascension to rank of "physician Lt Colonel" in the storm troopers, and his professional services in Lodz, Poland. It is true that there is no currently available evidence of Dr. Wegener as a direct perpetrator in the heinous crimes of the National Socialist regime in the Warthegau or elsewhere in Nazi occupied Europe. Nevertheless we believe Dr. Wegener is morally accountable for choosing to join the Nazi organizations liable for expounding a philosophy of violence inevitably producing mass murder; and for remaining publicly silent about events until his death in 1990. By such silence, he chose to remain a bystander rather than bear witness to the genocidal crimes he observed in the Lodz ghetto. Bearing witness may have been all the Jewish and Romany people, incarcerated, brutally mistreated in the ghetto, and inexorably annihilated, could have hoped for.
Dr. Wegener's motivation for joining the Nazi party and its storm troopers went to the grave with him. But to varying degrees, it may be attributed to the following: (1) careerism: Dr. Wegener's supervisor and academic mentor Martin Staemmler was one of the major proponents of the regime3; (2) family ties: his brother was a devoted party member who rose to the rank of "general" in the Schutzstaffel (SS) and Gauleiter; and (3) scientific appeal: the Nazi biological agenda had great allure for physicians, anthropologists, and biologists. This agenda, and careerism, attracted almost 50% of German physicians to join the Nazi party. Regardless of motive, Dr. Wegener chose to join organizations dedicated to the racial agenda that led to the industrialized genocide carried out by the Third Reich. He then chose silence.
Dr. Wegener's Nazi party membership and storm trooper rank distinguishes him from drafted German citizens serving in the armed forces during World War II. During his 6-year tenure in Poland as an army and health office pathologist, Dr. Wegener's office was three blocks from the Lodz ghetto. The ghetto included Jews from Lodz as well as Jews and Romany deported from the Reich. They were incarcerated, starved, and utilized as slave labor before their deportation to the Chelmno death camp. There they died in mobile gas vans, and their ashes were scattered in the woods. It is inconceivable that Dr. Wegener did not see nor know what awaited those human queues in Lodz, boarding the open trains to take them on their last journey.
After 1945, Dr. Wegener resumed his career at the first opportunity, continued his work, and avoided ever publicly commenting on what he had surely seen and knew of. We comprehend silence during the Nazi regime as there is no moral requirement for heroism. However, it would have been far less courageous and evenly morally required to speak out at some time and place after the war. There was still risk, however. The German medical establishment even to this day favors silence, encouraging "not soiling one's own nest"4(nestverschmutzen). Some in the medical community who spoke out were in various ways ostracized, punished, or driven from their chosen professional paths and not only in Germany.5 As Dr. Wegener attained international acclaim, his testimony may have helped the German medical establishment finally free itself of its shameful past of the National Socialist era. In addition, he could have provided additional eyewitness evidence to further refute the revisionists and holocaust deniers. But Dr. Wegener chose silence.
For this, he must be held morally responsible and thus ineligible for honors from the ACCP or other organizations expressing humanitarian ideals. We urge the ACCP to retract the Master Clinician Award from Friedrich Wegener. We also encourage the replacement of the disease eponym Wegener syndrome with that of ANCA-positive vasculitis in all publications of the ACCP. Not only do Dr. Wegener's life choices more than justify this, but like so many eponyms it is either an inaccurate reflection of the contribution of the eponymee or an inapt expedient.6
The authors have no conflicts of interest to disclose.
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News About Tommy Gai Waterhouse
Sheer Style Leads Wyong Magic Millions Classic 2013 Odds
Following a win in the Ballarat Magic Millions Clockwise Classic last start Garry Frazer's promising filly Sheer Style has opened top of the 2013 Wyong Magic Millions 2YO Classic odds this afternoon.
The field for this year's $200,000 Wyong Magic Millions 2YO Classic (1100m) is down to just six hopes following the early scratching of the Gai Waterhouse-trained ...
Unencumbered Heads 2013 Wyong Magic Millions 2YO Classic Field
Bjorn Baker's smart young colt Unencumbered will get another chance to further enhance their Gold Coast Magic Millions 2YO Classic 2014 claims when they race in the Wyong edition of the restricted feature this Thursday.
One of just seven juveniles racing in ...


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Step by Step through the Rapture DVD
Hosted by John Ankerberg
In this four program series Dr. Renald Showers and Dr. Jimmy DeYoung investigate what the Bible teaches about the timing and events that will take place when Jesus returns at the Rapture.
Please note: this product comes with a standard DVD disc and Blu-ray disc containing the same program material.
- Length: 1 hour 42 minutes
- Code: V67D


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LYONS, John 'Jack' - Peacefully on Tuesday January, 10 2017 after a brief illness, Dad passed in his 90th year.
Reunited with the love of his life, Ellen (Mary Ellen – 2011).
Loving father of Patricia (Edward) and Kathleen (Cari).
He will be deeply missed by grandsons William and Jack and the woman he considered a daughter Kim Hennick (Mary Sist).
Lovingly remembered by brother Robert (Leone) in Northern Ireland and sister-in-law Barbara in Georgia (the late Samuel).
Fondly remembered by nieces and nephews in Canada, England, Scotland, the United States, and relations around the world.
Cremation. Internment at North York at a later date.
As expressions of sympathy, donations to the West Parry Sound Health Centre Foundation or a charity of your choice would be appreciated by the family.


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a system of communication by telephone or radio inside an office, plane, etc.; the device you press or switch on to start using this system to announce something over the intercom They called him on the intercom. Mr Jack's arrival was announced over the intercom. See related entries: Communication devices, Parts of a plane Word OriginSecond World War: abbreviation of intercommunication.nounjump to other results
BrE BrE//ˈɪntəkɒm//; NAmE NAmE//ˈɪntərkɑːm//Communication devices, Parts of a plane
Check pronunciation: intercom


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Mix (12+): orders of 12+ items
Availability: In stock.
The Woodstock winery is located in McLaren Vale, just south of Adelaide. Whilst it is very much an Australian producer, its name actually derives from England. In the quaint town of Woodstock in Oxfordshire stands a set of wooden leg stocks. In Medieval times, these stocks were used to punish the local townsfolk who had over imbibed. As a cheeky nod to the town's history, Woodstock has named its flagship wine, the 'Stocks' Shiraz.
Tasting Notes: Bright colour and lifted aromas of cherries, plums and raspberries. Tempranillo adds dried spice notes to the nose and savoury tannins to the palate. Lively and expressive on both nose and palate, this Grenache Tempranillo blend captures true McLaren Vale characters; silky structure with subtle complexities making it a great food companion.
Medium body in style with a long lingering finish and classic Grenache texture. Cellaring will also be rewarded for 4 to 5 years as the secondary characters evolve in bottle. Enjoy with a selection of Spanish tapas or a nice smoked duck on the BBQ.
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April 16, 2014 15:15The Portland Police K-9 Unit officer shot in the line of duty this morning is Officer Jeff Dorn, a 16-year-veteran of the Bureau assigned to the K-9 Unit Night Shift. Officer Dorn has consented to the release of his name prior to the standard 24-hour waiting period as his family has been notified of his injuries.
Media Relations:
Sgt. Pete Simpson
Desk: 503-823-0830
Pager: 503-790-1779
Portland Police Bureau
1111 SW 2nd Ave, Suite 1526
Portland, Oregon 97204


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- 1/4 cup butter or margarine
- 1/4 cup all-purpose flour
- 3 cups milk
- 1 (10-ounce) block Cheddar cheese, shredded
- 1/2 cup thinly sliced green onions
- 1 teaspoon salt
- 1/4 teaspoon pepper
- 4 pounds potatoes, thinly sliced
- 1 1/2 cups soft breadcrumbs
- 1/4 cup butter or margarine, melted
- 1/4 cup grated Parmesan cheese
How to Make It
Melt 1/4 cup butter in a large saucepan over medium heat. Whisk in flour, and cook, whisking constantly, 2 to 3 minutes or until flour is lightly browned. Whisk milk into butter mixture; bring to a boil. Reduce heat, and simmer 6 minutes or until thickened. Stir in Cheddar cheese and next 3 ingredients, stirring until cheese melts.
Spread 1/4 cup cheese sauce evenly in a lightly greased 13- x 9-inch baking dish. Layer half of potatoes over sauce; top with half of remaining cheese sauce. Repeat with remaining potatoes and cheese sauce.
Bake at 325°, covered, 1 1/2 to 2 hours.
Stir together breadcrumbs, melted butter, and Parmesan cheese; spread evenly over potatoes. Bake, uncovered, 20 to 30 more minutes or until potatoes are tender.


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Supplying Model Makers since 1895
Telephone us on: 01508 549330
The Arsenal Football Club Puzzle Ball is the ideal present for all Arsenal supporters.
It provides the challenge of a traditional jigsaw with the benefit of creating collectable memorabilia of one of the UK's most prestigious football clubs, perfect for football fans young or old.
The Arsenal FC Puzzle Ball features images of the 2014/2015. Players including Theo Walcott, Alexis and team manager, Arsene Wenger.
The 3D Puzzle Ball is made up of 240 pieces and stands 8.6 inches high, the size of an actual football!
Once the challenge of piecing the Puzzle together is complete the winning creation can be displayed on its own display stand.
The Puzzle Ball is dated and each season a new Arsenal Football Club Puzzle Ball will be released so why not kick off your own memorabilia collection today!
The 3D Puzzle Ball is perfect for individuals who like a challenge and is great for banishing school holiday boredom or makes a fantastic rainy day activity.
This is a unique gift with a difference - perfect for any Arsenal fan or anyone who enjoys football and a bit of a challenge!
240 curved puzzle pieces
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Michigan Merit High School Graduation Requirements
The State Board of Education introduced the new high school graduation requirements, called the Michigan Merit Curriculum, in December 2005. When the state legislature enacted the requirements as law in March 2006, they became the driving force behind high school reform in Michigan. In satisfying these requirements, graduates will meet and often surpass the most stringent entry requirements for all colleges and universities in the state, and will provide the academic and applied learning skills business and industry leaders expect in their workforce.
Michigan Department of Education
website provides the most up to date information regarding the Michigan Merit High School Graduation Requirements.
Personal Curriculum
personal curriculum (PC)
provides a way to modify certain areas of the Michigan Merit Curriculum standard requirements. If all of the requirements for a PC are met, then the board of the school district may award a high school diploma to a student who successfully completes his or her PC, even if it does not meet the requirements of the Michigan Merit Curriculum standard.
The PC will be considered only after all other options have been attempted, and must meet stringent requirements for determining educational goals and evaluating student progress.
KRESA PC Handbook
provides information and forms to help guide you in creating a PC.
The Algebra II Quick Reference
helps explain the Algebra II requirements and modifications. Please note the modifications that go into effect July 1, 2011 or beginning with the graduating class of 2012.
For additional modification information and PC requirements, visit the Michigan Department of Education
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website or see the
Here is the most current amendment to
Revised School Code
These amendments are effective July 1, 2011.
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In Crime's Way: A Generation of Secret Service Adventures
A retired Special Agent in Charge of the United States Secret Service's special anti-counterfeiting detail in New York and author of the bestseller Undercover, Carmine J. Motto has lived a long and storied life. From witnessing a triple execution at New York's notorious Sing-Sing prison to thwarting an assassination attempt on the life of Harry Truman, Motto's name would make the list of any Law Enforcement Hall of Fame. In fact, so renowned are his exploits, that they were portrayed in a 20th Century Fox motion picture starring Burt Lancaster as Motto (Mr. 880).Now, readers can learn all about the real-life experiences of this "Top Cop." In Crime's Way: A Generation of Secret Service Adventures, is a series of true, authentic and fascinating stories of Motto's 60 years in law enforcement bringing counterfeiters, conspirators and scoundrels to justice. Follow his colorful career from police officer to secret service agent as he tells about being a cop in New York the night of the famous Orson Wells's "Invasion from Mars" radio broadcast, tracking a suspect who murdered his parents for their life insurance, or showing up to arrest a suspect, only to find himself as the witness for the man's marriage.While the book is written by and is about Motto, he is not the central character, but can be viewed almost like a narrator. Motto observes and participates in the action, but the real story is about the people he encounters. Most are presented in their own environment and situations of their own making as a result of their pursuit of an "easy dollar." No hot pursuits, exploding cars, or gun battles here. With his remarkable aptitude for story telling, Motto has preserved actual stories of life and the underworld as he saw it from his position as a renowned counterfeit investigator.
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Financing: $75,000.00 Down and TBD
Reason for Selling:Changing careers
Located in the heart of town for over 20 years, this bike shop is well known in the neighborhood for friendly, reliable service. This is an authorized dealer for Giant, Schwinn, GT, KHS, Phat Cycles, Nirve Cruisers, Electra Bikes, Kent, Verde BMX, Kink BMS and other major parts and accessory manufacturers. Special order bikes or repairs on site. This seller is motivated - bring your ideas to break into the up and coming sport of cycling. Call Patricia for more details 631-942-0506
Disclaimer: The information in these listings have been provided by the Seller and Vested Business Brokers, Ltd. has not independently verified this information and has no responsibility for its accuracy or completeness. Vested recommends that you consult with an accountant, attorney and other professionals to represent and assist you in any transaction. Vested's associate brokers will arrange a meeting between you, your representatives and the Seller, but is not authorized to make any representations or warranties. You agree to hold Vested and its associated brokers harmless of any misrepresentations made by seller.
1(877) 735-5224 | 50 Karl Ave Smithtown, NY 11787 | vestedbb.com


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Now, you don't need to deal with odd computer and router problems, as we are here to provide the best online technical support to wretched computer users in the USA, Canada and UK.
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We've helped over 50,000 individual clients with their technical support issues each year, and we have been growing at a steady rate. Read More
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There are many companies which are offering the online computer support services, but they are unable to satisfy their customers and in the end, clients bear huge loss of time and money. But, we are here to provide you the world-class services, anytime you need. We have customized all our services based upon the customers need. Read More
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We offer the best computer support services online or by email. We love to accept the challenges and our main challenge is to provide our clients 100% satisfaction computer support. We are working 24x7 to provide you excellent technical support round the clock.
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Berkley Gulp! Leech and Jumbo Leech Soft Baits
Sale price: Clearance price: Starting at: Sale starting at: Clearance starting at: Reg. price:
The versatile Berkley® Gulp!® Leech and Jumbo Leech baits attract anything that swims and are an excellent choice for walleye or as a jig trailer when you're flipping for bass. Their unique fluttering action that fish attack is combined with the natural, 100% organic flavor and powerful scent of Berkley Gulp!. This deadly combination produces a leech that not only tastes and feels alive, it acts alive too.
- Acts like a live leech
- Unique fluttering action fish attack
- Natural, 100% organic flavor
- Powerful scent of Berkley Gulp!
- Leeches are a preferred food of walleye
- Excellent as a jig trailer when flipping for bass
"The Gulp! Jumbo Leech is proven to outfish a live leech--it has a bigger head to hold hooks better, and its meaty profile is irresistible to walleye.''
Keith Kavajecz, Legendary Walleye Angler
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i really like the color of the leech #8 i see it in the picture but don't see it in the sales order list why's that?
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Douglas M. Dillard, 61, died Thursday, Nov. 8, 2001.
Mr. Dillard was a son of the late M.B. and Evelyn Austin Dillard. He was a retired foreman at Lucent Technology.
Services will be at 2 p.m. today at Smith Funeral Home with Wade Grizzle officiating.
Survivors include his wife, Catherine Culver Dillard; a daughter, Angie Dillard, Statham; a son, Jeff Dillard, Statham; a sister, Brenda Smith, Bethlehem; two brothers, Melvin Dillard, Jacksonville, Fla., and Dwight Dillard, Decatur; and four grandchildren.
Athens Banner-Herald, Saturday, Nov. 10, 2001
Published in the Athens Banner-Herald on Saturday, November 10, 2001.
© 2017. All Rights Reserved. | Contact Us


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ANP Photography ProductionPhotographer Male Singapore, Singapore, Singapore
My Website: ANP ProductionsMy MM URL: http://www.modelmayhem.com/anpsingapore
Mayhem # 778419
About Me
ANP Photography Production has been involved in photography, videography and digital art for more than 10 years in Singapore.
Have you worked with ANP Photography Production? Add Credits for ANP Photography Production! >>
"Fantastic photographer, always ready to guide and share."
Worked together more than 20 times; Most recent: April 2015
Elen Moore
Worked together 2-10 times; Most recent: February 2013
"Very professional photographer and nice gentleman I am always comfortable and glad to shoot. He is really lord of the light :-) and made amazing portraits for me. Always pleasure to work with and my highest recommendations to other models."
See Sample Worked together 2-10 times; Most recent: February 2016
See Sample Worked together 1 times; Most recent: February 2012
Credit Notes
Jen, Aileen, Fiona, ShaSha, Jaslyn, Diana, Alicia, Angelique, Jade, Jessica, Sharon, Zoey, Jean, Xiaotin, Raychell, Olivia, Anna, Naomi, Ellis, Naomi, Charmaine, Pam, Samantha, Michelle, Miimji, Emily, Zylia, Gwen, Katherine, Sally, Carol, Brandy, Yolanda, Hannah, Jas, Anhelika.
jvnne Model
Ellis_Ning Model
Zylia Ang Model
monogram Photographer
Jake Roth - Pathways Photographer
-lyss v- Model
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Victor I Am the Spirit MP3
Victor I Am the Spirit (Alex Lifeson)
Hajtkovič feat. Majk Spirit, Nironic - I AM THE ONE MP3
NOVÝ ALBUM V PREDAJI TU http://shop.rukahore.sk/ypsilon www.majkspirit.sk http://www.facebook.com/DjHajtkovic.
T.D. Jakes-Sermons 2015 - The Spirit Speaks & I Am The Good Shepherd MP3
"The Spirit Speaks" & "I Am The Good Shepherd" in August 21+23 ,2015 -T.D. Jakes-Sermons 2015 00:00:01 ▻▻ Bishop T.D. Jakes 2015, -"The Spirit Speaks" ...
Hajtkovic feat. Majk Spirit, Nironic - I am the one MP3
I am the spirit of the harvest moon. MP3
Just who do you think you are?
Dota 2 | I AM THE GOD DAMN THUNDERGOD!! | Baumi plays Storm Spirit MP3
I also stream Dota on Twitch! http://www.twitch.tv/l34um1 --------------------------------------------------------------------------- JOIN THE GIVEAWAY!! Commentors on ...
Davood Azad - Im the spirit moon MP3
Music: Davood Azad, Bach ( Prelude in E minor) Lyrics: Jalāl ad-Dīn Muhammad Balkhī ( Rumi ) I am the Spirit Moon You do not see me for I am hidden inside ...
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See exclusive trailers for The Flash and Arrow after the season finale of Vixen this Tuesday, September 29th on CW Seed: http://bit.ly/vixen106 For more on ...
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A artful journey with the spirit within each of us To Lady Liza Jane Sassy the Playa Angel urantiaresearch.com.
I AM THE DREAM - The Spirit of Culrav (by Evil Genius) MP3
Culrav - The Cultural Fest of MNNIT Allahabad I AM THE DREAM - The promotional video showing the Spirit of Culrav :) A Film by - ANSHUL SHARMA CAST ...
The Script - Hall of Fame ft. will.i.am MP3
The Script's official music video for 'Hall of Fame'. Click to listen to The Script on Spotify: http://smarturl.it/TheScriptSpotify?IQid=ScriptHOF As featured on #3.
Hall Of Fame
The ScriptYeah, you could be the greatest You can be the best You can be the King Kong banging on your chest You could beat the world You could beat the war You could talk to God, go banging on His door You can throw your hands up You can beat the clock You can move a mountain You can break rocks Some will[...]
Spirit Alora - I Am The Voice MP3
PLEASE READ Through the time I last poted an animated vid "Raina unbeautiful" I've struggled with this one around the mid point,My carlpe tunel came back full ...
Dubai, Spirit of Dubai Video (HD) 2015 MP3
Dubai Video: Welcome to The Spirit of Dubai Video! Dubai is one of those cities that must be seen to be believed. Watch this video and discover the true spirit of ...
The Spirit Of Dark And Lonely Water (Public Information Film 1973) MP3
Lonely Water (widely known as The Spirit of Dark and Lonely Water) is a 1973 British Public Information short film Voiced By Donald Pleasence.
Here I Am - Spirit: Stallion of the Cimarron MP3
Please give suggestions and/or requests... ? Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes ...
Introitus - I am the Spirit that Caressed Eternity MP3
Album: Skies of the Unholy Departed (1996.) Pioneers of Serbian Black Metal.
here i am spirit video MP3
this is the melodious song form the movie SPIRIT. this song is sung by Brayan Adams...........
Spirit - Here I Am(HD) MP3
spirit here i am lyrics MP3
here i am lyrics from the film spirit enjoy!!! :)
Majk spirit - I am the one MP3
Spirit - Here I Am (Bryan Adams's Song) MP3
Here I am - this is me There's no where else on earth I'd rather be Here I am - it's just me and you Tonight we make our dreams come true It's a new world - it's a ...
Cherokee Morning Song: "I am of the Great Spirit" MP3
Cherokee Morning Song by Walela. The words "We n' de ya ho" mean "I am of the Great Spirit" Vintage photographs of various Native Americans in traditional ...
I am the Spirit of a Woman - Barkha Dhar.wmv MP3
Barkha Dhar's Facebook Page: http://www.facebook.com/pages/Barkha-Dhars-Blogs-On-Social-Issues/170810456288444 This is a poem written by Barkha ...
Davod Azad , Rumi & Bach...I am the Spirit Moon MP3
من آن ماهم که اندر لامکانم مجو بیرون مرا در عین جانم تو را هر کس به سوی خویش خواند تو را من جز به سوی تو نخوان...
As I AM: The Fruit of the Spirit Part 3/10 MP3
Taste and see that the Lord is good! A program to encourage anyone that has the ears to hear, the heart to grow, and the mind to learn in their day to day living.
DreamWorks - Spirit: Stallion of the Cimarron (Bryan Adams) - Here I Am - Lyrics MP3
DreamWorks Bryan Adams Spirit: The Stallion Of The Cimarron ''Here I Am'' Images Song Lyrics.