Work

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

Year: 2021

Type: conference-paper

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.

Source: SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling

DOI: 10.1117/12.2581893

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