3D EAGAN: 3D edge-aware attention generative adversarial network for
  prostate segmentation in transrectal ultrasound images
Mengqing Liu Xiao Shao Liping Jiang Kaizhi Wu
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.