@inproceedings{fb7ef7f4ed7a4a288c62a243dda6a8ab,
title = "Automatic segmentation of the prostate on MR images based on anatomy and deep learning",
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.",
keywords = "Anatomy, Deep learning, Image segmentation, Location constraint, MRI, Prostate, Shape prior knowledge",
author = "Lei Tao and Ling Ma and Maoqiang Xie and Xiabi Liu and Zhiqiang Tian and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581893",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Linte, {Cristian A.} and Siewerdsen, {Jeffrey H.}",
booktitle = "Medical Imaging 2021",
address = "United States",
}
