Automatic prostate segmentation in TRUS images has always been a challenging problem, since prostates in TRUS images have ambiguous boundaries and inhomogeneous intensity distribution. Although many prostate segmentation methods have been proposed, they still need to be improved due to the lack of sensibility to edge information. Consequently, the objective of this study is to devise a highly effective prostate segmentation method that overcomes these limitations and achieves accurate segmentation of prostates in TRUS images. A 3D edge-aware attention generative adversarial network (3D EAGAN)-based prostate segmentation method is proposed in this paper, which consists of an edge-aware segmentation network (EASNet) that performs the prostate segmentation and a discriminator network that distinguishes predicted prostates from real prostates. The proposed EASNet is composed of an encoder-decoder-based U-Net backbone network, a detail compensation module, four 3D spatial and channel attention modules, an edge enhance module, and a global feature extractor. The detail compensation module is proposed to compensate for the loss of detailed information caused by the down-sampling process of the encoder. The features of the detail compensation module are selectively enhanced by the 3D spatial and channel attention module. Furthermore, an edge enhance module is proposed to guide shallow layers in the EASNet to focus on contour and edge information in prostates. Finally, features from shallow layers and hierarchical features from the decoder module are fused through the global feature extractor to predict the segmentation prostates.

翻译：经直肠超声图像中的自动前列腺分割一直是一个具有挑战性的问题，因为图像中的前列腺边界模糊且强度分布不均匀。尽管已有许多前列腺分割方法被提出，但由于缺乏对边缘信息的敏感性，其性能仍需改进。因此，本研究的目的是设计一种高效的前列腺分割方法，克服这些局限性，实现经直肠超声图像中前列腺的精准分割。本文提出了一种基于三维边缘感知注意力生成对抗网络的前列腺分割方法，其中包含执行前列腺分割的边缘感知分割网络与判别预测前列腺与真实前列腺的判别器网络。所提出的边缘感知分割网络由编码器-解码器结构的U-Net主干网络、细节补偿模块、四个三维空间与通道注意力模块、边缘增强模块及全局特征提取器组成。细节补偿模块用于补偿编码器下采样过程造成的细节信息损失，其输出特征通过三维空间与通道注意力模块进行选择性增强。此外，边缘增强模块被提出以引导边缘感知分割网络的浅层网络聚焦于前列腺的轮廓与边缘信息。最后，浅层特征与解码器模块的层级特征通过全局特征提取器融合，以预测前列腺分割结果。