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Automatic prostate segmentation in TRUS images has always been a challengingproblem, since prostates in TRUS images have ambiguous boundaries andinhomogeneous intensity distribution. Although many prostate segmentationmethods have been proposed, they still need to be improved due to the lack ofsensibility to edge information. Consequently, the objective of this study isto devise a highly effective prostate segmentation method that overcomes theselimitations and achieves accurate segmentation of prostates in TRUS images. A3D edge-aware attention generative adversarial network (3D EAGAN)-basedprostate segmentation method is proposed in this paper, which consists of anedge-aware segmentation network (EASNet) that performs the prostatesegmentation and a discriminator network that distinguishes predicted prostatesfrom real prostates. The proposed EASNet is composed of anencoder-decoder-based U-Net backbone network, a detail compensation module,four 3D spatial and channel attention modules, an edge enhance module, and aglobal feature extractor. The detail compensation module is proposed tocompensate for the loss of detailed information caused by the down-samplingprocess of the encoder. The features of the detail compensation module areselectively enhanced by the 3D spatial and channel attention module.Furthermore, an edge enhance module is proposed to guide shallow layers in theEASNet to focus on contour and edge information in prostates. Finally, featuresfrom shallow layers and hierarchical features from the decoder module are fusedthrough the global feature extractor to predict the segmentation prostates.

3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images