Wang Xinyu, Chang Zhengqi, Zhang Qingfang, Li Cheng, Miao Fei, Gao Gang
College of Information Science and Technology, Northwest University, Xi'an 710127, China.
College of Computer Science and Technology, Xidian University, Xi'an 710071, China.
Biomedicines. 2023 Feb 21;11(3):646. doi: 10.3390/biomedicines11030646.
In recent years, the incidence of prostate cancer in the male population has been increasing year by year. Transrectal ultrasound (TRUS) is an important means of prostate cancer diagnosis. The accurate segmentation of the prostate in TRUS images can assist doctors in needle biopsy and surgery and is also the basis for the accurate identification of prostate cancer. Due to the asymmetric shape and blurred boundary line of the prostate in TRUS images, it is difficult to obtain accurate segmentation results with existing segmentation methods. Therefore, a prostate segmentation method called DSU-Net is proposed in this paper. This proposed method replaces the basic convolution in the U-Net model with the improved convolution combining shear transformation and deformable convolution, making the network more sensitive to border features and more suitable for prostate segmentation tasks. Experiments show that DSU-Net has higher accuracy than other existing traditional segmentation methods.
近年来，男性人群中前列腺癌的发病率逐年上升。经直肠超声（TRUS）是前列腺癌诊断的重要手段。在TRUS图像中准确分割前列腺可辅助医生进行穿刺活检和手术，也是准确识别前列腺癌的基础。由于TRUS图像中前列腺形状不对称且边界线模糊，采用现有的分割方法难以获得准确的分割结果。因此，本文提出了一种名为DSU-Net的前列腺分割方法。该方法用结合了剪切变换和可变形卷积的改进卷积取代了U-Net模型中的基本卷积，使网络对边界特征更加敏感，更适合前列腺分割任务。实验表明，DSU-Net比其他现有的传统分割方法具有更高的准确性。
