Comparative Analysis of U-Net and U-Net3 + for Retinal Exudate Segmentation: Performance Evaluation Across Regions.
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...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 6; pp. 4129 - 4138 |
|---|---|
| Main Authors: | , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2025
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190236349&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190236349 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Dec2025 vid: 38 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 190236349 189894351 190236349 190236349 10.1007/s10278-025-01419-4 190236349 ppf: 4129 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparative Analysis of U-Net and U-Net3 + for Retinal Exudate Segmentation: Performance Evaluation Across Regions. aug: au: Kang, Yeong Hun Kim, Young Jae Kim, Kwang Gi affil: https://ror.org/03ryywt80 Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam, Korea sug: subj: Diabetic Retinopathy Diagnosis Disease Progression Severity of Illness Evaluation Exudates and Transudates Radiography Retina Radiography Image Processing, Computer Assisted Methods Deep Learning Methods Sensitivity and Specificity Evaluation Human Study Design T-Tests Analysis of Variance Data Analysis Software Descriptive Statistics Funding Source ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|