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 journalArticle

21 Scopus citations

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.

Original languageEnglish (US)
Article number245015
JournalPhysics in Medicine and Biology
Volume63
Issue number24
DOIs
StatePublished - Dec 14 2018

Keywords

  • CT images
  • deep learning
  • fully automated
  • organ segmentation
  • organs at risk
  • prostate

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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