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Segmentation of Choroidal Area in Optical Coherence Tomography Images Using a Transfer Learning-Based Conventional Neural Network: A Focus on Diabetic Retinopathy and a Literature Review Publisher Pubmed

Summary: Can AI aid eye health? Study finds DeepLabv3+SE excels in choroid segmentation for diabetic retinopathy. #DiabeticRetinopathy #DeepLearning

Saeidian J1 ; Azimi H1 ; Azimi Z2 ; Pouya P3 ; Asadigandomani H4 ; Riaziesfahani H4 ; Hayati A5 ; Daneshvar K4 ; Khalili Pour E4
Authors

Source: BMC Medical Imaging Published:2024


Abstract

Background: This study aimed to evaluate the effectiveness of DeepLabv3+with Squeeze-and-Excitation (DeepLabv3+SE) architectures for segmenting the choroid in optical coherence tomography (OCT) images of patients with diabetic retinopathy. Methods: A total of 300 B-scans were selected from 21 patients with mild to moderate diabetic retinopathy. Six DeepLabv3+SE variants, each utilizing a different pre-trained convolutional neural network (CNN) for feature extraction, were compared. Segmentation performance was assessed using the Jaccard index, Dice score (DSC), precision, recall, and F1-score. Binarization and Bland-Altman analysis were employed to evaluate the agreement between automated and manual measurements of choroidal area, luminal area (LA), and Choroidal Vascularity Index (CVI). Results: DeepLabv3+SE with EfficientNetB0 achieved the highest segmentation performance, with a Jaccard index of 95.47, DSC of 98.29, precision of 98.80, recall of 97.41, and F1-score of 98.10 on the validation set. Bland-Altman analysis indicated good agreement between automated and manual measurements of LA and CVI. Conclusions: DeepLabv3+SE with EfficientNetB0 demonstrates promise for accurate choroid segmentation in OCT images. This approach offers a potential solution for automated CVI calculation in diabetic retinopathy patients. Further evaluation of the proposed method on a larger and more diverse dataset can strengthen its generalizability and clinical applicability. © The Author(s) 2024.
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