Fully automated organ segmentation in male pelvic CT images

Anjali Balagopal, Samaneh Kazemifar, Dan Nguyen, Mu Han Lin, Raquibul Hannan, Amir Owrangi, Steve Jiang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - May 31 2018

ASJC Scopus subject areas

  • General

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