Abstract
Purpose: Despite the tremendous success achieved by deep learning (DL) based algorithms in organ segmentation, automatic rectum segmentation in high-dose rate brachytherapy (HDRBT) of cervical cancer treatment planning remains a challenging problem due to the lack of a well-defined boundary between the rectum and sigmoid colon. Existing DL-based algorithms cannot accurately segment the superior portion of the rectum, leading to large errors in D2cc computation, since the superior part of rectum often falls in the high dose region. To address this problem, we developed a three-stage segmentation framework to accurately determine the recto-sigmoid junction and improve rectum segmentation for HDRBT treatment planning.
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