期刊
出版社
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2581893
关键词
Prostate; image segmentation; deep learning; anatomy; location constraint; shape prior knowledge; MRI
类别
资金
- Natural Science Foundation of Tianjin [18JCYBJC15700]
- China Postdoctoral Science Foundation [2018M641635]
- Fundamental Research Funds for the Central Universities [60973059]
- National Natural Science Foundation of China [81171407]
This study proposes an automatic segmentation method for the prostate on MR images based on anatomy knowledge, achieving a high segmentation accuracy in experiments.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据