Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion

Ling Ma, Rongrong Guo, Guoyi Zhang, Funmilayo Tade, David M. Schuster, Peter Nieh, Viraj Master, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Martin A. Styner, Elsa D. Angelini
ISBN (Electronic)9781510607118
StatePublished - 2017
Externally publishedYes
EventMedical Imaging 2017: Image Processing - Orlando, United States
Duration: Feb 12 2017Feb 14 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2017: Image Processing
Country/TerritoryUnited States


  • Computed tomography (CT)
  • Convolutional neural networks (CNN)
  • Deep learning
  • Image segmentation
  • Multiatlas segmentation
  • Prostate
  • Prostate cancer

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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