Fully automated organ segmentation in male pelvic CT images. (14th December 2018)

• Record Type: Journal Article
• Authors: Balagopal, Anjali; Kazemifar, Samaneh; Nguyen, Dan; Lin, Mu-Han; Hannan, Raquibul; Owrangi, Amir; Jiang, Steve
• Is Part Of: Physics in medicine & biology. Volume 63:Number 24(2018:Dec.)
• Publication Date: 2018-12-14
• Subjects: prostate -- organs at risk -- organ segmentation -- CT images -- deep learning -- fully automated; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aaf11c
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D organ volume localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset comprising 136 patients. Test results show that 3D U-Net based segmentation achieves mean (±SD) Dice coefficient values of 90 (±2.0)%, 96 (±3.0)%, 95 (±1.3)%, 95 (±1.5)%, and 84 (±3.7)% for prostate, left femoral head, right femoral head, bladder, and rectum, respectively, using the proposed fully automated segmentation method.