We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes. The process is performed in three steps, including organ detection, lesion detection and lesion segmentation. Our method applies machine learning techniques such as marginal space learning and convolutional neural networks, as well as active contour models. The method proves to be robust in its handling of extremely high lesion diversity. We tested our method on volumetric computed tomography (CT) images, including 42 volumes containing liver lesions and 86 volumes containing 595 pathological lymph nodes. Preliminary results under 10-fold cross validation show that for both the liver lesions and the lymph nodes, a total detection sensitivity of 0.53 and average Dice score of @math for segmentation were obtained.
@cite_4: The objective of this work was to develop and validate algorithms for detection and classification of hypodense hepatic lesions, specifically cysts, hemangiomas, and metastases from CT scans in the portal venous phase of enhancement. Fifty-six CT sections from 51 patients were used as representative of common hypodense liver lesions, including 22 simple cysts, 11 hemangiomas, 22 metastases, and 1 image containing both a cyst and a hemangioma. The detection algorithm uses intensity-based histogram methods to find central lesions, followed by liver contour refinement to identify peripheral lesions. The classification algorithm operates on the focal lesions identified during detection, and includes shape-based segmentation, edge pixel weighting, and lesion texture filtering. Support vector machines are then used to perform a pair-wise lesion classification. For the detection algorithm, 80 lesion sensitivity was achieved at approximately 0.3 false positives (FP) per slice for central lesions, and 0.5 FP per slice for peripheral lesions, giving a total of 0.8 FP per section. For 90 sensitivity, the total number of FP rises to about 2.2 per section. The pair-wise classification yielded good discrimination between cysts and metastases (at 95 sensitivity for detection of metastases, only about 5 of cysts are incorrectly classified as metastases), perfect discrimination between hemangiomas and cysts, and was least accurate in discriminating between hemangiomas and metastases (at 90 sensitivity for detection of hemangiomas, about 28 of metastases were incorrectly classified as hemangiomas). Initial implementations of our algorithms are promising for automating liver lesion detection and classification.
@cite_8: 
@cite_3: This paper presents a method for extracting lymph node regions from 3-D abdominal CT images using 3-D minimum directional difference filter. In the case of surgery of colonic cancer, resection of metastasis lesions is performed with resection of a primary lesion. Lymph nodes are main route of metastasis and are quite important for deciding resection area. Diagnosis of enlarged lymph nodes is quite important process for surgical planning. However, manual detection of enlarged lymph nodes on CT images is quite burden task. Thus, development of lymph node detection process is very helpful for assisting such surgical planning task. Although there are several report that present lymph node detection, these methods detect lymph nodes primary from PET images or detect in 2-D image processing way. There is no method that detects lymph nodes directly from 3-D images. The purpose of this paper is to show an automated method for detecting lymph nodes from 3-D abdominal CT images. This method employs a 3-D minimum directional difference filter for enhancing blob structures with suppressing line structures. After that, false positive regions caused by residua and vein are eliminated using several kinds of information such as size, blood vessels, air in the colon. We applied the proposed method to three cases of 3-D abdominal CT images. The experimental results showed that the proposed method could detect 57.0 of enlarged lymph nodes with 58 FPs per case.
@cite_0: Accurate knowledge of the liver structure, including liver surface and lesion localization, is usually required in treatments such as liver tumor ablations and or radiotherapy. This paper presents a new method and corresponding algorithm for fast segmentation of the liver and its internal lesions from CT scans. No interaction between the user and analysis system is required for initialization since the algorithm is fully automatic. A statistical model-based approach was created to distinguish hepatic tissue from other abdominal organs. It was combined to an active contour technique using gradient vector flow in order to obtain a smoother and more natural liver surface segmentation. Thereafter, automatic classification was performed to isolate hepatic lesions from liver parenchyma. Twenty-one datasets, presenting different anatomical and pathological situations, have been processed and analyzed. Special focus has been driven to the resulting processing time together with quality assessment. Our method allowed robust and efficient liver and lesion segmentations very close to the ground truth, in a relatively short processing time (average of 11.4 s for a 512 × 512-pixel slice). A volume overlap of 94.2 and an accuracy of 3.7 mm were achieved for liver surface segmentation. Sensitivity and specificity for tumor lesion detection were 82.6 and 87.5 , respectively.
@cite_2: In this paper, we propose a novel machine learning approach for locating boundaries of liver tumors in CT (Computed Tomography) images. Given a marker indicating a rough location of a tumor, the proposed solution locates its boundary. Our approach consists of training process and locating process. In training process, we train AdaBoosted histogram classifiers to classify true boundary positions and false ones on the 1-D intensity profiles of tumor regions. In locating process, we locate the boundaries by using the trained AdaBoosted histogram classifiers. The novelty of our approach is that we use AdaBoost in the training process to learn diverse intensity distributions of the tumor regions, and utilize the trained results successfully in locating process. Experimental results show our approach locates the boundaries successfully, despite the diverse intensity distributions of the tumor regions, marker location variability and tumor region shape variability. Our framework is also generic and can be applied for locating boundaries of blob-like targets with diverse intensity distributions in other applications.
@cite_5: 
@cite_11: Tumor detection in CT liver images is a challenging task. The nature of tumor has a direct effect on the number of voxels being contaminated, as well as on the changes in the observed CT scan. In order to deal with this challenge, in this paper we propose the use of advanced non-linear machine learning techniques to determine the optimal features, as well as the hyperplane that use these features to separate tumoral voxels from voxels corresponding to healthy tissues. Very promising classification results using an important volume of clinically annotated data (86 sensitivity, 82 specificity) demonstrate the potentials of our approach.
Previous work directed toward automatic lymph node detection is limited @cite_8 @cite_3 . A special filter was used in @cite_3 to detect lymph nodes. This minimum directional difference (Min-DD) filter is constructed with the assumption that lymph nodes have uniform intensity, which is not always the case. The Min-DD filter method @cite_3 was improved in @cite_8 by adding a Hessian-based blobness measure for reducing false positives. Several automatic algorithms have been proposed for liver lesion detection and segmentation, including combinations of adaptive multi-thresholding and morphological operators @cite_4 or k-means clustering on mean shift filtered images @cite_0 . However, these histogram-based methods require a good contrast between lesions and parenchyma. Other techniques, such as AdaBoost, have been used in both semi-automatic approaches @cite_2 and in automatic settings to classify image textures @cite_11 . In another approach, Shimizu @cite_5 trained two AdaBoost classifiers with a set of statistical and gradient features, as well as with features based on a convergence index filter that enhances blob-like structures. Our work is innovative due to the following: