Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.

Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. The proposed model extends standard conditional generative adversarial networks. Additionally to the discriminator which enforces the model to create realistic organ delineations, it embeds cascaded partially pre-trained convolutional encoder-decoders as generator. Encoder fine-tuning from a large amount of non-medical images alleviates data scarcity limitations. The network is trained end-to-end to benefit from simultaneous multi-level segmentation refinements using auto-context. Employed for healthy liver, kidneys and spleen segmentation, our pipeline provides promising results by outperforming state-of-the-art encoder-decoder schemes. Followed for the Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge organized in conjunction with the IEEE International Symposium on Biomedical Imaging 2019, it gave us the first rank for three competition categories: liver CT, liver MR and multi-organ MR segmentation. Combining cascaded convolutional and adversarial networks strengthens the ability of deep learning pipelines to automatically delineate multiple abdominal organs, with good generalization capability. The comprehensive evaluation provided suggests that better guidance could be achieved to help clinicians in abdominal image interpretation and clinical decision making.

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance are hard to interpret. This makes comparative analysis a necessary tool towards interpretable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal semantic segmentation tasks has been rarely discussed. In order to expand the knowledge on these topics, the CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Abdominal organ segmentation from routine acquisitions plays an important role in several clinical applications, such as pre-surgical planning or morphological and volumetric follow-ups for various diseases. These applications require a certain level of performance on a diverse set of metrics such as maximum symmetric surface distance (MSSD) to determine surgical error-margin or overlap errors for tracking size and shape differences. Previous abdomen related challenges are mainly focused on tumor/lesion detection and/or classification with a single modality. Conversely, CHAOS provides both abdominal CT and MR data from healthy subjects for single and multiple abdominal organ segmentation. Five different but complementary tasks were designed to analyze the capabilities of participating approaches from multiple perspectives. The results were investigated thoroughly, compared with manual annotations and interactive methods. The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0.98 ± 0.00 / 0.95 ± 0.01), but the best MSSD performance remains limited (21.89 ± 13.94 / 20.85 ± 10.63 mm). The performances of participating models decrease dramatically for cross-modality tasks both for the liver (DICE: 0.88 ± 0.15 MSSD: 36.33 ± 21.97 mm). Despite contrary examples on different applications, multi-tasking DL models designed to segment all organs are observed to perform worse compared to organ-specific ones (performance drop around 5%). Nevertheless, some of the successful models show better performance with their multi-organ versions. We conclude that the exploration of those pros and cons in both single vs multi-organ and cross-modality segmentations is poised to have an impact on further research for developing effective algorithms that would support real-world clinical applications. Finally, having more than 1500 participants and receiving more than 550 submissions, another important contribution of this study is the analysis on shortcomings of challenge organizations such as the effects of multiple submissions and peeking phenomenon.

Implementation and use of 3D pairwise geodesic distance fields for seeding abdominal aortic vessels.

PURPOSE: Precise extraction of aorta and the vessels departing from it (i.e. coeliac, renal, and iliac) is vital for correct positioning of a graft prior to abdominal aortic surgery. To perform this task, most of the segmentation algorithms rely on seed points, and better-located seed points provide better initial positions for cross-sectional methods. Under non-optimal acquisition characteristics of daily clinical routine and complex morphology of these vessels, inserting seed points to all these small, but critically important vessels is a tedious, time-consuming, and error-prone task. Thus, in this paper, a novel strategy is developed to generate pathways between user-inserted seed points in order to initialize segmentation methods effectively. METHOD: The proposed method requires only a single user-inserted seed for each vessel of interest for initializations. Starting from these initial seeds, it automatically generates pathways that span all vessels in between. To accomplish this, first, a geodesic mask is generated by adaptive thresholding, which reinforces the initial seeds to be kept in the vascular tree. Then, a novel implementation of 3D pairwise geodesic distance field (3D-PGDF) is utilized. It is shown that the minimal-valued geodesic of 3D-PGDF successfully defines a path linking the initial seeds as being the shortest geodesic. Moreover, the robustness of the minimum level set of the 3D-PGDF to local variations and regions of high curvature is increased by a region classification strategy, which adds partial geodesics to these critical regions. RESULTS: The proposed method was applied to 19 challenging CT data sets obtained from four different scanners and compared to two benchmark methods. The first method is a high-precision technique with very long processing time (subvoxel precise multi-stencil fast marching-MSFM), while the second is a very fast method with lower accuracy (3D fast marching). The results, which are obtained using various measures, show that the pathways generated by the developed technique enable significantly higher segmentation performance than 3D fast marching and require much less computational power and time than MSFM. CONCLUSION: The developed technique offers a useful tool for generating pathways between seed points with minimal user interaction. It guarantees to include all important vessels in a computationally effective manner and thus, it can be used to initialize segmentation methods for abdominal aortic tree.

Semi-automatic segmentation methods for 3-D visualization and analysis of the liver.

Pre-evaluation of donors prior to surgery of living donated liver transplantation is one of the challenging applications that computer aided systems are needed. The precise measurement of liver volume requires effective segmentation procedures, while three dimensional rendering of the segmented data provides demonstrative information to radiologists and surgeons before surgery. The Insight Toolkit provides effective algorithms for segmentation, which are also optimized for high computational performance and processing time. Furthermore, An ITK pipeline can be combined with a VTK pipeline, so that the result of segmentation can be represented directly in 3-D using VTK. Therefore, there is an on-going trend for developing ITK/VTK based systems. This study presents quantitative and qualitative performance evaluation of two effective ITK algorithms on segmentation of liver from CTA data sets.

Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation.

Identifying liver region from abdominal computed tomography-angiography (CTA) data sets is one of the essential steps in evaluation of transplantation donors prior to the hepatic surgery. However, due to gray level similarity of adjacent organs, injection of contrast media and partial volume effects; robust segmentation of the liver is a very difficult task. Moreover, high variations in liver margins, different image characteristics with different CT scanners and atypical liver shapes make the segmentation process even harder. In this paper, we propose a three stage (i.e. pre-processing, classification, post-processing); automatic liver segmentation algorithm that adapts its parameters according to each patient by learning the data set characteristics in parallel to segmentation process to address all the challenging aspects mentioned above. The efficiency in terms of the time requirement and the overall segmentation performance is achieved by introducing a novel modular classification system consisting of a K-Means based simple classification system and an MLP based complex one which are combined with a data-dependent and automated switching mechanism that decides to apply one of them. Proposed approach also makes the design of the overall classification system fully unsupervised that depends on the given CTA series only without requiring any given training set of CTA series. The segmentation results are evaluated by using area error rate and volume calculations and the success rate is calculated as 94.91% over a data set of diverse CTA series of 20 patients according to the evaluation of the expert radiologist. The results show that, the proposed algorithm gives better results especially for atypical liver shapes and low contrast studies where several algorithms fail.