Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks

Kelei He, Xiaohuan Cao, Yinghuan Shi, Dong Nie, Yang Gao, Dinggang Shen

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Accurate segmentation of pelvic organs (i.e., prostate, bladder, and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to: 1) low soft tissue contrast in CT images and 2) large shape and appearance variations of pelvic organs. In this paper, we employ a two-stage deep learning-based method, with a novel distinctive curve-guided fully convolutional network (FCN), to solve the aforementioned challenges. Specifically, the first stage is for fast and robust organ detection in the raw CT images. It is designed as a coarse segmentation network to provide region proposals for three pelvic organs. The second stage is for fine segmentation of each organ, based on the region proposal results. To better identify those indistinguishable pelvic organ boundaries, a novel morphological representation, namely, distinctive curve, is also introduced to help better conduct the precise segmentation. To implement this, in this second stage, a multi-task FCN is initially utilized to learn the distinctive curve and the segmentation map separately and then combine these two tasks to produce accurate segmentation map. The final segmentation results of all three pelvic organs are generated by a weighted max-voting strategy. We have conducted exhaustive experiments on a large and diverse pelvic CT data set for evaluating our proposed method. The experimental results demonstrate that our proposed method is accurate and robust for this challenging segmentation task, by also outperforming the state-of-the-art segmentation methods.

Original languageEnglish
Article number8451958
Pages (from-to)585-595
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume38
Issue number2
DOIs
Publication statusPublished - 2019 Feb 1

Keywords

  • computed tomography
  • Image segmentation
  • multitasking
  • neural networks
  • pelvic organ
  • prostate cancer

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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