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Optical coherence tomography (OCT) is one of the non-invasive andeasy-to-acquire biomarkers (the thickness of the retinal layers, which isdetectable within OCT scans) being investigated to diagnose Alzheimer's disease(AD). This work aims to segment the OCT images automatically; however, it is achallenging task due to various issues such as the speckle noise, small targetregion, and unfavorable imaging conditions. In our previous work, we haveproposed the multi-stage & multi-discriminatory generative adversarial network(MultiSDGAN) to translate OCT scans in high-resolution segmentation labels. Inthis investigation, we aim to evaluate and compare various combinations ofchannel and spatial attention to the MultiSDGAN architecture to extract morepowerful feature maps by capturing rich contextual relationships to improvesegmentation performance. Moreover, we developed and evaluated a guidedmutli-stage attention framework where we incorporated a guided attentionmechanism by forcing an L-1 loss between a specifically designed binary maskand the generated attention maps. Our ablation study results on the WVU-OCTdata-set in five-fold cross-validation (5-CV) suggest that the proposedMultiSDGAN with a serial attention module provides the most competitiveperformance, and guiding the spatial attention feature maps by binary masksfurther improves the performance in our proposed network. Comparing thebaseline model with adding the guided-attention, our results demonstratedrelative improvements of 21.44% and 19.45% on the Dice coefficient and SSIM,respectively.

Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Training