{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T02:21:52Z","timestamp":1770085312815,"version":"3.49.0"},"reference-count":18,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation","award":["B04G640070"],"award-info":[{"award-number":["B04G640070"]}]},{"name":"Thailand\u2019s Education Hub for the Southern Region of ASEAN Countries for Ph.D. Students"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The Indian Diabetic Retinopathy Image Dataset (IDRiD) has been widely adopted for DR lesion segmentation research. However, it contains annotation gaps for proliferative DR lesions and labeling errors that limit its utility for comprehensive automated screening systems. We present Refined IDRiD, an enhanced version that addresses these limitations through (1) expert ophthalmologist validation and correction of labeling errors in original annotations for four non-proliferative lesions (microaneurysms, hemorrhages, hard exudates, cotton-wool spots), (2) the addition of three critical proliferative DR lesion annotations (neovascularization, vitreous hemorrhage, intraretinal microvascular abnormalities), and (3) the integration of comprehensive anatomical context (optic disc, fovea, blood vessels, retinal region). A team of three ophthalmologists (one senior specialist with &gt;10 years\u2019 experience, two expert fundus image annotators) conducted systematic annotation refinement, achieving an inter-rater agreement F1-score of 0.9012. The enhanced dataset comprises 81 high-resolution fundus images with pixel-level annotations for seven DR lesion types and four anatomical structures. All images were cropped to the retinal region of interest and resized to 1024 \u00d7 1024 pixels, with annotations stored as unified grayscale masks containing 12 classes enabling efficient multi-task learning. Refined IDRiD enables training of comprehensive DR screening systems capable of detecting both non-proliferative and proliferative stages while reducing false positives through anatomical context awareness.<\/jats:p>","DOI":"10.3390\/data11020030","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T12:49:44Z","timestamp":1770036584000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Refined IDRiD: An Enhanced Dataset for Diabetic Retinopathy Segmentation with Expert-Validated Annotations and Comprehensive Anatomical Context"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9823-3517","authenticated-orcid":false,"given":"Sakon","family":"Chankhachon","sequence":"first","affiliation":[{"name":"College of Digital Science, Prince of Songkla University, Songkhla 90110, Thailand"}]},{"given":"Supaporn","family":"Kansomkeat","sequence":"additional","affiliation":[{"name":"Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4015-1941","authenticated-orcid":false,"given":"Patama","family":"Bhurayanontachai","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2018-222X","authenticated-orcid":false,"given":"Sathit","family":"Intajag","sequence":"additional","affiliation":[{"name":"Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.2147\/CIA.S297494","article-title":"Diabetic Retinopathy in the Aging Population: A Perspective of Pathogenesis and Treatment","volume":"16","author":"Leley","year":"2021","journal-title":"Clin. Interv. Aging"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., and Meriaudeau, F. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research. Data, 3.","DOI":"10.3390\/data3030025"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic Assessment of Deep Learning Algorithms for Diabetic Retinopathy Screening","volume":"501","author":"Li","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","article-title":"TeleOphta: Machine Learning and Image Processing Methods for Teleophthalmology","volume":"34","author":"Zhang","year":"2013","journal-title":"IRBM"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a Publicly Distributed Database: The MESSIDOR Database","volume":"33","author":"Cazuguel","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kalviainen, H., and Pietil\u00e4, J. (2007). DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol, Department of Ophthalmology, Faculty of Medicine, University of Kuopio. Technical Report.","DOI":"10.5244\/C.21.15"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TMI.2020.3037771","article-title":"A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability","volume":"40","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chankhachon, S., Kansomkeat, S., Bhurayanontachai, P., and Intajag, S. (2025). Deep Learning Network with Illuminant Augmentation for Diabetic Retinopathy Segmentation Using Comprehensive Anatomical Context Integration. Diagnostics, 15.","DOI":"10.3390\/diagnostics15212762"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Early Treatment Diabetic Retinopathy Study Research Group (1991). Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs\u2014An Extension of the Modified Airlie House Classification. ETDRS Report Number 10. Ophthalmology, 98, 786\u2013806.","DOI":"10.1016\/S0161-6420(13)38012-9"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., Soto-Rey, I., and Kramer, F. (2022). Towards a Guideline for Evaluation Metrics in Medical Image Segmentation. BMC Res. Notes, 15.","DOI":"10.1186\/s13104-022-06096-y"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_13","first-page":"379","article-title":"Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks","volume":"Volume 10541","author":"Wang","year":"2017","journal-title":"Machine Learning in Medical Imaging, MLMI 2017"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fu, Y., Liu, M., Zhang, G., and Peng, J. (2024). Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation. Appl. Sci., 14.","DOI":"10.3390\/app14166941"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"120987","DOI":"10.1016\/j.eswa.2023.120987","article-title":"RMCA U-net: Hard Exudates Segmentation for Retinal Fundus Images","volume":"234","author":"Fu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107971","DOI":"10.1016\/j.patcog.2021.107971","article-title":"Optic Disc Segmentation by U-net and Probability Bubble in Abnormal Fundus Images","volume":"117","author":"Fu","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108711","DOI":"10.1016\/j.patcog.2022.108711","article-title":"Fovea Localization by Blood Vessel Vector in Abnormal Fundus Images","volume":"129","author":"Fu","year":"2022","journal-title":"Pattern Recognit."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/11\/2\/30\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T13:18:09Z","timestamp":1770038289000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/11\/2\/30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,1]]},"references-count":18,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["data11020030"],"URL":"https:\/\/doi.org\/10.3390\/data11020030","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,1]]}}}