Automatic segmentation of the prostate on MR images based on anatomy and deep learning

Lei Tao; Ling Ma; Maoqiang Xie; Xiabi Liu; Zhiqiang Tian; Baowei Fei

doi:10.1117/12.2581893

15 February 2021 Automatic segmentation of the prostate on MR images based on anatomy and deep learning

Lei Tao, Ling Ma, Maoqiang Xie, Xiabi Liu, Zhiqiang Tian, Baowei Fei

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115981N (2021) https://doi.org/10.1117/12.2581893
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Accurate segmentation of the prostate has many applications in the detection, diagnosis and treatment of prostate cancer. Automatic segmentation can be a challenging task because of the inhomogeneous intensity distributions on MR images. In this paper, we propose an automatic segmentation method for the prostate on MR images based on anatomy. We use the 3D U-Net guided by anatomy knowledge, including the location and shape prior knowledge of the prostate on MR images, to constrain the segmentation of the gland. The proposed method has been evaluated on the public dataset PROMISE2012. Experimental results show that the proposed method achieves a mean Dice similarity coefficient of 91.6% as compared to the manual segmentation. The experimental results indicate that the proposed method based on anatomy knowledge can achieve satisfactory segmentation performance for prostate MRI.

Conference Presentation

Citation Download Citation

Lei Tao, Ling Ma, Maoqiang Xie, Xiabi Liu, Zhiqiang Tian, and Baowei Fei "Automatic segmentation of the prostate on MR images based on anatomy and deep learning", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115981N (15 February 2021); https://doi.org/10.1117/12.2581893

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