학술논문
Fully Automated Organ Segmentation in Male Pelvic CT Images
Document Type
Working Paper
Author
Source
Balagopal A, Kazemifar S, Nguyen D, Lin M H, Hannan R, Owrangi A and Jiang S 2018 Fully automated organ segmentation in male pelvic CT images Phys. Med. Biol. 63 245015
Subject
Language
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 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.
Comment: 21 pages; 11 figures; 4 tables
Comment: 21 pages; 11 figures; 4 tables