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A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying Tv-Unet for Rpe Layer Detection Publisher



Saeedizadeh N1 ; Tajmirriahi M1 ; Haghani A1 ; Amini Z1 ; Pour EK2 ; Riaziesfahani H2 ; Fadakar K2 ; Kafieh R3 ; Rabbani H1
Authors
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Authors Affiliations
  1. 1. School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, 81746734641, Iran
  2. 2. Tehran University of Medical Sciences, Farabi Eye Hospital, Retina Ward, Tehran, 1416634793, Iran
  3. 3. Durham University, Department of Engineering, Durham, DH1 3LE, United Kingdom

Source: IEEE Transactions on Instrumentation and Measurement Published:2022


Abstract

Retinal optical coherence tomography (OCT) images are widely used in diagnosis of ocular conditions. However, random shift and orientation changes of the retinal layers in OCT B-scans yield to appearance variations across the scans. These variations reduce the accuracy of the algorithms applied in the analysis of OCT images. In this study, we propose a preprocessing step to compensate these variations and align B-scans. At first, by incorporating total variation (TV) loss in the well-known Unet model, we propose a TV-Unet model to accurately detect the retinal pigment epithelium (RPE) layer in each B-scan. Then, we use the detected RPE layer in the alignment method to form a curvature curve and a reference line. A novel window transferring-based alignment approach is applied to force the curve points to form a straight line, while preserving the shape and size of the pathological lesions. Since the detection of RPE layer is a crucial step in the proposed alignment method, we utilized various datasets to train and test the TV-Unet and provided a multimodal, device-independent OCT image alignment method. The TV-Unet localizes the RPE layer in OCT images with low boundary error (maximum of 1.94 pixels) and high Dice coefficient (minimum of 0.98). Quantitative and qualitative results indicated that the proposed method can efficiently detect the RPE layer and align OCT images while preserving the structure and size of the retinal lesions (biomarkers) in the OCT scans. © 1963-2012 IEEE.
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