Automatic segmentation of abdominal vessels for improved pancreas localization

Amal Farag; Jiamin Liu; Ronald M. Summers

doi:10.1117/12.2043867

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11 March 2014 Automatic segmentation of abdominal vessels for improved pancreas localization

Amal Farag, Jiamin Liu, Ronald M. Summers

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Proceedings Volume 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment; 90371M (2014) https://doi.org/10.1117/12.2043867
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Accurate automatic detection and segmentation of abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis as well as computer-aided diagnosis. The large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges making the task very difficult. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. Due to the close proximity of the pancreas to numerous surrounding organs within the abdominal cavity the organ shifts according to the conditions of the organs within the abdomen, as such the pancreas is constantly changing. Combining these challenges with typically found patient-to-patient variations and scanning conditions the pancreas becomes harder to localize. In this paper we focus on three abdominal vessels that almost always abut the pancreas tissue and as such useful landmarks to identify the relative location of the pancreas. The splenic and portal veins extend from the hila of the spleen and liver, respectively, travel through the abdominal cavity and join at a position close to the head of the pancreas known as the portal confluence. A third vein, the superior mesenteric vein, anastomoses with the other two veins at the portal confluence. An automatic segmentation framework for obtaining the splenic vein, portal confluence and superior mesenteric vein is proposed using 17 contrast enhanced computed-tomography datasets. The proposed method uses outputs from the multi-organ multi-atlas label fusion and Frangi vesselness filter to obtain automatic seed points for vessel tracking and generation of statistical models of the desired vessels. The approach shows ability to identify the vessels and improve localization of the pancreas within the abdomen.

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Amal Farag, Jiamin Liu, and Ronald M. Summers "Automatic segmentation of abdominal vessels for improved pancreas localization", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90371M (11 March 2014); https://doi.org/10.1117/12.2043867

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