Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks.

ABSTRACT: Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentatio

SUBMITTER: Gibson E 

PROVIDER: S-EPMC6076994 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature