Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.
磁共振（MR）成像技术是前列腺癌诊断的重要工具，通过计算机辅助诊断技术准确分割磁共振前列腺区域对于前列腺癌的诊断具有重要意义。本文使用深度学习方法，对传统V型网络（V-Net）网络进行了改进，提出了一种改进的端到端的三维图像分割网络，以期提供更精确的图像分割结果。本文首先将软注意力机制融合进传统V-Net的跳跃连接中，结合短跳跃连接、小卷积核进一步提升网络分割精度。然后使用前列腺MR图像分割评估2012年挑战赛（PROMISE 12）数据集，针对前列腺区域进行了分割，使用戴斯相似系数（DSC）和豪斯多夫距离（HD）对模型进行了评估，分割模型的DSC值和HD值分别可达到0.903和3.912 mm。实验结果表明，本文算法能够提供更准确的三维分割结果，可以准确高效地分割前列腺MR图像，为临床诊断和治疗提供可靠的依据。.
Keywords: Attention mechanism; Image segmentation; Magnetic resonance; Prostate; V-Net.