通过自动准确地分割男性盆腔磁共振成像（MRI）图像中的前列腺和前列腺癌，可以实现基于影像导向的临床诊断。对于手术患者来说，前列腺癌的位置、数量和大小非常关键，以确保准确的肿瘤切除。前列腺和肿瘤区域之间的形态差异很小，肿瘤的大小不确定，肿瘤与周围组织之间的边界模糊，将正常区域与肿瘤分开的分类不均匀。因此，在MRI图像上分割前列腺和肿瘤具有挑战性。 本研究提出了一种基于双分支注意力驱动多尺度学习的前列腺和前列腺癌分割网络。首先，双分支结构提供了两个不同尺度的输入图像用于特征编码，以及一个多尺度注意力模块，用于收集不同尺度的细节。然后，将双分支结构的特征输入到构建的特征融合模块中，以获得更完整的上下文信息。最后，为了提供更精确的学习表示，每个阶段都使用深度监督机制进行构建。 我们提出的网络在多种男性盆腔MRI数据集上进行的前列腺和肿瘤分割的结果显示，其表现优于现有技术。对于前列腺和前列腺癌的MRI分割，Dice相似系数（DSC）的值分别为91.65%和84.39%。 我们的方法在自动分割结果和专家手动分割结果之间保持了高度的相关性和一致性。准确的自动分割前列腺和前列腺癌在临床上具有重要的意义。 版权所有 © 2023 Elsevier Ltd. 保留所有权利。

Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging.This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism.The results of our proposed network's prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively.Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance.Copyright © 2023 Elsevier Ltd. All rights reserved.