Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy.

ABSTRACT: Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for organ boundary and use it as the context information to guide the segmentation. Specifically, we design a two-stage learning strategy in the proposed BCnet: 1) Boundary cod

SUBMITTER: Wang S 

PROVIDER: S-EPMC8202780 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature