Open AccessAutomatic segmentation of the prostate on MR images based on anatomy and deep learning
Author(s) -
Tao Lei,
Ling Ma,
Minzhu Xie,
Xiabi Liu,
Zhiqiang Tian,
Baowei Fei
Publication year - 2021
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2581893
Subject(s) - segmentation , computer science , artificial intelligence , image segmentation , prostate , prostate gland , computer vision , similarity (geometry) , prostate cancer , sørensen–dice coefficient , pattern recognition (psychology) , image (mathematics) , medicine , cancer
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.