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Proceedings Paper

Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion
Author(s): Ling Ma; Rongrong Guo; Guoyi Zhang; Funmilayo Tade; David M. Schuster; Peter Nieh; Viraj Master; Baowei Fei
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Paper Abstract

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.

Paper Details

Date Published: 24 February 2017
PDF: 9 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332O (24 February 2017); doi: 10.1117/12.2255755
Show Author Affiliations
Ling Ma, Emory Univ. (United States)
Beijing Institute of Technology (China)
Rongrong Guo, Emory Univ. (United States)
Guoyi Zhang, Emory Univ. (United States)
Funmilayo Tade, Emory Univ. (United States)
David M. Schuster, Emory Univ. (United States)
Peter Nieh, Emory Univ. (United States)
Viraj Master, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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