  Optical coherence tomography (OCT) is one of the non-invasive and
easy-to-acquire biomarkers (the thickness of the retinal layers, which is
detectable within OCT scans) being investigated to diagnose Alzheimer's disease
(AD). This work aims to segment the OCT images automatically; however, it is a
challenging task due to various issues such as the speckle noise, small target
region, and unfavorable imaging conditions. In our previous work, we have
proposed the multi-stage & multi-discriminatory generative adversarial network
(MultiSDGAN) to translate OCT scans in high-resolution segmentation labels. In
this investigation, we aim to evaluate and compare various combinations of
channel and spatial attention to the MultiSDGAN architecture to extract more
powerful feature maps by capturing rich contextual relationships to improve
segmentation performance. Moreover, we developed and evaluated a guided
mutli-stage attention framework where we incorporated a guided attention
mechanism by forcing an L-1 loss between a specifically designed binary mask
and the generated attention maps. Our ablation study results on the WVU-OCT
data-set in five-fold cross-validation (5-CV) suggest that the proposed
MultiSDGAN with a serial attention module provides the most competitive
performance, and guiding the spatial attention feature maps by binary masks
further improves the performance in our proposed network. Comparing the
baseline model with adding the guided-attention, our results demonstrated
relative improvements of 21.44% and 19.45% on the Dice coefficient and SSIM,
respectively.
