Multi-organ segmentation of male pelvic CT using dual attention networks

Abstract

The delineation of the prostate and organs-at-risk (OARs) is fundamental to radiation treatment planning, but is currently labor-intensive and observer-dependent. We aimed to develop an automated computed tomography (CT)-based multiorgan (bladder, prostate, rectum, left and right femoral heads) segmentation method for prostate radiation therapy treatment planning. The proposed method generated synthetic MRI (sMRI) to offer superior soft-tissue information for male pelvic CT images. Cycle-consistent adversarial networks (CycleGAN) were used to generate CT-based sMRIs. A dual attention network (DAN) extracted features from both CTs and sMRIs. A deep attention strategy was integrated into the DAN to select the most relevant features from both CTs and sMRIs to identify organ boundaries. The CT-based sMRI generated from our previously trained CycleGAN and its corresponding CT images were inputted to the proposed DAN to provide complementary information. The proposed method was trained and evaluated using datasets from 40 patients with prostate cancer, and were then compared against state-of-art methods. The Dice similarity coefficients between our results and ground truth were 0.95±0.05, 0.88±0.08, 0.90±0.04, 0.95±0.04, and 0.95±0.04 for bladder, prostate, rectum, left and right femoral heads, respectively. The proposed method could be used in routine prostate cancer radiotherapy treatment planning to rapidly segment the prostate and standard OARs.