Feb 1, 2017

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

Proceedings of SPIE
Ling MaBaowei Fei

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 applic...Continue Reading

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Mentioned in this Paper

INTS6
Prostatic Neoplasms
Biologic Segmentation
PSCA protein, human
PCGEM1 non-coding RNA, human
Neural Network Simulation
Prostate
Atlases
Depth
Learning

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