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 journalArticlepeer-review

80 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

Bibliographical note

Funding Information:
Manuscript received August 15, 2018; accepted August 23, 2018. Date of publication August 30, 2018; date of current version February 1, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61432008, Grant 61673203, and Grant U1435214, in part by the Young Elite Scientists Sponsorship Program through CAST under Grant 2016QNRC001, in part by NIH under Grant CA206100, and in part by the Collaborative Innovation Center of Novel Software Technology and Industrialization. (Corresponding authors: Dinggang Shen; Yang Gao.) K. He is with the State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210008, China, and also with the Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599 USA.

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61432008, Grant 61673203, and Grant U1435214, in part by the Young Elite Scientists Sponsorship Program through CAST under Grant 2016QNRC001, in part by NIH under Grant CA206100, and in part by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

Publisher Copyright:
© 2018 IEEE.

Keywords

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

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks'. Together they form a unique fingerprint.

Cite this