Journal
MEDICAL IMAGING 2021: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING
Volume 11598, Issue -, Pages -Publisher
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2581893
Keywords
Prostate; image segmentation; deep learning; anatomy; location constraint; shape prior knowledge; MRI
Categories
Funding
- 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]
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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.
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