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Optimal Deep Learning Architecture for Automated Segmentation of Cysts in Oct Images Using X-Let Transforms Publisher



Darooei R1, 2 ; Nazari M3, 4 ; Kafieh R1, 5 ; Rabbani H1, 2
Authors
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Authors Affiliations
  1. 1. Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran
  2. 2. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran
  3. 3. Department of Molecular Biology and Genetics, Aarhus University, Aarhus, 8200, Denmark
  4. 4. The Danish Research Institute of Translational Neuroscience (DANDRITE), Aarhus University, Aarhus, 8200, Denmark
  5. 5. Department of Engineering, Durham University, South Road, Durham, DH1 3RW, United Kingdom

Source: Diagnostics Published:2023


Abstract

The retina is a thin, light-sensitive membrane with a multilayered structure found in the back of the eyeball. There are many types of retinal disorders. The two most prevalent retinal illnesses are Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). Optical Coherence Tomography (OCT) is a vital retinal imaging technology. X-lets (such as curvelet, DTCWT, contourlet, etc.) have several benefits in image processing and analysis. They can capture both local and non-local features of an image simultaneously. The aim of this paper is to propose an optimal deep learning architecture based on sparse basis functions for the automated segmentation of cystic areas in OCT images. Different X-let transforms were used to produce different network inputs, including curvelet, Dual-Tree Complex Wavelet Transform (DTCWT), circlet, and contourlet. Additionally, three different combinations of these transforms are suggested to achieve more accurate segmentation results. Various metrics, including Dice coefficient, sensitivity, false positive ratio, Jaccard index, and qualitative results, were evaluated to find the optimal networks and combinations of the X-let’s sub-bands. The proposed network was tested on both original and noisy datasets. The results show the following facts: (1) contourlet achieves the optimal results between different combinations; (2) the five-channel decomposition using high-pass sub-bands of contourlet transform achieves the best performance; and (3) the five-channel decomposition using high-pass sub-bands formations out-performs the state-of-the-art methods, especially in the noisy dataset. The proposed method has the potential to improve the accuracy and speed of the segmentation process in clinical settings, facilitating the diagnosis and treatment of retinal diseases. © 2023 by the authors.
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