Arikan, Mustafa;
Willoughby, James;
Ongun, Sevim;
Sallo, Ferenc;
Montesel, Andrea;
Ahmed, Hend;
Hagag, Ahmed;
... Dubis, Adam M; + view all
(2025)
OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers.
Nature Scientific Data
, 12
, Article 267. 10.1038/s41597-024-04259-z.
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Abstract
Publicly available open-access OCT datasets for retinal layer segmentation have been limited in scope, often being small in size, specific to a single disease, or containing only one grading. This dataset improves upon this with multi-grader and multi-disease labels for training machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection). This dataset compiled 5016 pixel-wise manual labels for 1672 OCT scans featuring 5 layer boundaries for three different disease classes to support development of automatic techniques. A subset of data (566 scans across 9 classes of disease biomarkers) was subsequently labeled for disease features for 4698 bounding box annotations. To minimize bias, images were shuffled and distributed among graders. Retinal layers were corrected, and outliers identified using the interquartile range (IQR). This step was iterated three times, improving layer annotations’ quality iteratively, ensuring a reliable dataset for automated retinal image analysis.
| Type: | Article |
|---|---|
| Title: | OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41597-024-04259-z |
| Publisher version: | https://doi.org/10.1038/s41597-024-04259-z |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| Keywords: | Biomarkers, Biomedical engineering, Machine learning, Scientific data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10201383 |
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