Tao Lei, Ma Ling, Xie Maoqiang, Liu Xiabi, Tian Zhiqiang, Fei Baowei
College of Software, Nankai University, Tianjin, China.
School of Computer Science, Beijing Institute of Technology, Beijing, China.
Proc SPIE Int Soc Opt Eng. 2021 Feb;11598. doi: 10.1117/12.2581893. Epub 2021 Feb 15.
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.
前列腺的精确分割在前列腺癌的检测、诊断和治疗中有许多应用。由于磁共振图像上强度分布不均匀，自动分割可能是一项具有挑战性的任务。在本文中，我们提出了一种基于解剖学的磁共振图像前列腺自动分割方法。我们使用由解剖学知识引导的3D U-Net，包括前列腺在磁共振图像上的位置和形状先验知识，来约束腺体的分割。所提出的方法已在公开数据集PROMISE2012上进行了评估。实验结果表明，与手动分割相比，所提出的方法实现了91.6%的平均骰子相似系数。实验结果表明，所提出的基于解剖学知识的方法能够在前列腺磁共振成像中获得令人满意的分割性能。
