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<p>Diabetic retinopathy is a major complication of diabetes, with its prevalence nearly doubling to approximately 10.5% by 2021. Exudates, the characteristic lesions of diabetic retinopathy, are crucial for assessing disease progression and severity. The location and distribution of these exudates can affect various regions of the retina, necessitating a detailed regional analysis of lesions. To address this need, this study aimed to evaluate the performance of exudate detection in fundus images across various regions, including perivascular and extravascular areas, perifoveal and extrafoveal regions, and in quadrants defined relative to the fovea. We employed U-net and U-net3 + deep learning models for validation, evaluating their performance using accuracy, sensitivity, specificity, and Dice score. Overall, the U-net3 + model outperformed the U-net model. Therefore, the performance evaluation was based on the results from the U-net3 + model. Comparing the detection performance across perivascular versus extravascular and perifoveal versus extrafoveal regions, the U-net3 + model achieved highest Dice score in the extravascular (87.96% [± 5.80]) and perifoveal areas (88.03% [± 5.86]). Additionally, superior sensitivity and Dice scores were observed in the top-left and top-right quadrants. Future research is anticipated to show that deep learning-based automatic exudate detection will enhance diagnostic accuracy and efficiency, leading to better treatment and prognosis in patients with diabetic retinopathy.</p>

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Comparative Analysis of U-Net and U-Net3 + for Retinal Exudate Segmentation: Performance Evaluation Across Regions

  • 期刊论文

  • 发表日期：

    2025年2月4日

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  • Yeong Hun Kang,
  • Young Jae Kim,
  • Kwang Gi Kim

摘要

Diabetic retinopathy is a major complication of diabetes, with its prevalence nearly doubling to approximately 10.5% by 2021. Exudates, the characteristic lesions of diabetic retinopathy, are crucial for assessing disease progression and severity. The location and distribution of these exudates can affect various regions of the retina, necessitating a detailed regional analysis of lesions. To address this need, this study aimed to evaluate the performance of exudate detection in fundus images across various regions, including perivascular and extravascular areas, perifoveal and extrafoveal regions, and in quadrants defined relative to the fovea. We employed U-net and U-net3 + deep learning models for validation, evaluating their performance using accuracy, sensitivity, specificity, and Dice score. Overall, the U-net3 + model outperformed the U-net model. Therefore, the performance evaluation was based on the results from the U-net3 + model. Comparing the detection performance across perivascular versus extravascular and perifoveal versus extrafoveal regions, the U-net3 + model achieved highest Dice score in the extravascular (87.96% [± 5.80]) and perifoveal areas (88.03% [± 5.86]). Additionally, superior sensitivity and Dice scores were observed in the top-left and top-right quadrants. Future research is anticipated to show that deep learning-based automatic exudate detection will enhance diagnostic accuracy and efficiency, leading to better treatment and prognosis in patients with diabetic retinopathy.