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

doi:10.1117/12.2255755

24 February 2017 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

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101332O (2017) https://doi.org/10.1117/12.2255755
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Citation Download Citation

Ling Ma, Rongrong Guo, Guoyi Zhang, Funmilayo Tade, David M. Schuster, Peter Nieh, Viraj Master, and Baowei Fei "Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332O (24 February 2017); https://doi.org/10.1117/12.2255755

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