To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort. However, when this concept is ported to the medical imaging domain, reading expertise will have a direct impact on the annotation accuracy. In this study, we examine the impact of expertise and the amount of available annotations on the accuracy outcome of a liver segmentation problem in an abdominal computed tomography (CT) image database. In controlled experiments, we study this impact for different types of weak annotations. To address the decrease in accuracy associated with lower expertise, we propose a method for outlier correction making use of a weakly labelled atlas. Using this approach, we demonstrate that weak annotations subject to high error rates can achieve a similarly high accuracy as state-of-the-art multi-atlas segmentation approaches relying on a large amount of expert manual segmentations. Annotations of this nature can realistically be obtained from a non-expert crowd and can potentially enable crowdsourcing of weak annotation tasks for medical image analysis.
@cite_5: An N-dimensional image is divided into “object” and “background” segments using a graph cut approach. A graph is formed by connecting all pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels (voxels) have to be a priori identified as object or background seeds providing necessary clues about the image content. Our objective is to find the cheapest way to cut the edges in the graph so that the object seeds are completely separated from the background seeds. If the edge cost is a decreasing function of the local intensity gradient then the minimum cost cut should produce an object background segmentation with compact boundaries along the high intensity gradient values in the image. An efficient, globally optimal solution is possible via standard min-cut max-flow algorithms for graphs with two terminals. We applied this technique to interactively segment organs in various 2D and 3D medical images.
@cite_7: We present a novel and fast interactive approach to multi-modality cardiac image segmentation, which employs the linearly ordered surfaces as an additional constraint. We show using such a geometrical constraint helps to significantly reduce user interaction and improve the accuracy of segmentation results at the same time. We solve the proposed multiregion segmentation problem with the order constraints by means of convex optimization, resulting in a fast and reliable flow maximization approach which implicitly embeds the linear order prior without introducing extra computation load. In this regard, a new fully parallelized continuous max-flow algorithm is proposed and implemented using GPGPU to segment a 3D volume within one second. We demonstrate our results over pathological trans-esophageal echocardiogram, cardiac CT and delayed enhancement MRI data sets.
@cite_6: Interactive segmentation is becoming of increasing interest in medical imaging, combining the positive aspects of manual and automated segmentation. However, general purpose tools have been lacking in terms of segmenting multiple regions with a high degree of coupling simultaneously. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. With generalized hierarchical max-flow solvers, the hierarchy is specified in run-time, allowing different hierarchies to be explored. This paper presents a novel interactive segmentation interface, using generalized hierarchical max-flow for multi-region segmentation.
@cite_2: Label ordering, the specification of subset---superset relationships for segmentation labels, has been of increasing interest in image segmentation as they allow for complex regions to be represented as a collection of simple parts. Recent advances in continuous max-flow segmentation have widely expanded the possible label orderings from binary background foreground problems to extendable frameworks in which the label ordering can be specified. This article presents Directed Acyclic Graph Max-Flow image segmentation which is flexible enough to incorporate any label ordering without constraints. This framework uses augmented Lagrangian multipliers and primal---dual optimization to develop a highly parallelized solver implemented using GPGPU. This framework is validated on synthetic, natural, and medical images illustrating its general applicability.
To reduce the annotation effort, many well-known studies propose segmentation methods employing simple forms of user annotations to obtain voxel-wise segmentations . While adjusting hyperparameters can be considered an interaction, in this study we concentrate on simplified forms of pictorial input , called weak annotations (WA). Such annotations have been extensively used in the literature, particularly in the context of medical object segmentation problems. @cite_5 used user-provided scribbles (SC) or brush strokes as input and hard constraints to an interactive graphical segmentation problem. Similarly, @cite_6 , @cite_2 and @cite_7 expressed this problem in a spatially continuous setting by using prior region ordering constraints and exploiting parallelism via GPU computing. The GrabCut algorithm employs rectangular regions (RR) as bounding boxes to both compute a colour appearance model and spatially constrain the search for an object. These spatial constraints further allow to reduce the computational effort . The segmentation platform ITK-SNAP combines RR with SC and employs a pre-segmentation (PS) to initialise an active contour model.