Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls
Open accessAbstract
This paper presents optical coherence tomography (OCT) images of the human retina and manual delineations of eight retinal layers. The data includes 35 human retina scans acquired on a Spectralis OCT system (Heidelberg Engineering, Heidelberg, Germany), 14 of which are healthy controls (HC) and 21 have a diagnosis of multiple sclerosis (MS). The provided data includes manually delineation of eight retina layers, which were independently reviewed and edited. The data presented in this article was used to validate automatic segmentation algorithms (Lang et al., 2013).
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The proposed system is evaluated on two public datasets, HC/MS and DME, and on an internal dataset of nAMD.
“The proposed system is evaluated on two public datasets, HC/MS [46] and DME [6] and on an internal dataset consisting of scans of eyes with neovascular AMD (nAMD) (Fig. 4).”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [46]
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Research works printed but not linked: 12
- [1]Lang A. Carass A. Hauser M. Sotirchos E.S. Calabresi P.A. Ying H.S. Prince J.L. Retinal layer segmentation of macular OCT images using boundary classification Biomed. Opt. Express 4 7 2013 1133 1152 23847738 PMC3704094
- [2]Carass A. Lang A. Hauser M. Calabresi P.A. Ying H.S. Prince J.L. Multiple-object geometric deformable model for segmentation of Macular. OCT Biomed. Opt. Express 5 4 2014 1062 1074 24761289 PMC3986003
- [3]He Y. Carass A. Yun Y. Zhao C. Jedynak B.M. Solomon S.D. Saidha S. Calabresi P.A. Prince J.L. Towards topological correct segmentation of macular OCT from cascaded FCNs Fetal, Infant and Ophthalmic Medical Image Analysis 2017 Springer 202 209 PMC6660164 31355372
- [4]Y. He, A. Carass, Y. Yun, C. Zhao, B.M. Jedynak, S.D. Solomon, P.A. Calabresi, J.L. Prince, Topology Guranteed Segmentation of the Human Retina from OCT Using Convolutional Neural Networks. arXiv preprint 〈arXiv:1803.05120〉 (2018). · arXiv
- [5]Y. Liu, A. Carass, S.D. Soloman, S. Saidha, P.A. Calabresi, J.L. Prince, Multi-layer fast level set segmentation for macular OCT, in: Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 1445–1448. PMC6919647 31853331
- [6]Lang A. Carass A. Bittner A.K. Ying H.S. Prince J.L. Improving graph based segmentation for severe pathology in Retinitis Pigmentosa patients Proc. SPIE Med. Imaging (SPIE-MI 2017) 2017 (pp. 10137-10137-8) PMC5540322 28781413
- [7]A. Lang, A. Carass, A.K. Bittner, H.S. Ying, J.L. Prince, Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace, in: Proceedings of the IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016), 2016, pp. 197–200. PMC5042207 27695603
- [8]Lang A. Carass A. Jedynak B.M. Solomon S.D. Saidha S. Calabresi P.A. Prince J.L. Intensity inhomogeneity correction of SD-OCT data using macular flatspace Med. Image Anal. 43 2018 85 97 29040910 PMC6311386
- [9]Bhargava P. Lang A. Al-Louzi O. Carass A. Prince J.L. Calabresi P.A. Saidha S. Applying an open-source segmentation algorithm to different OCT devices in multiple sclerosis patients and healthy controls Mult. Scler. Int. 2015 (Article ID 136295) PMC4452193 26090228
- [10]Chen M. Lang A. Ying H.S. Calabresi P.A. Prince J.L. Carass A. Analysis of Macular OCT images using deformable registration OCT. Biomed. Opt. Express 5 7 2014 2184 2195 PMC4102359 25071959
- [11]M. Chen, A. Lang, E. Sotirchos, H.S. Ying, P.A. Calabresi, J.L. Prince, A. Carass, Deformable registration of Macular OCT using A-mode scan similarity, in: Proceedings of the IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013), 2013, pp. 476–479. PMC3892764 24443687
- [12]Reaungamornrat S. Carass A. He Y. Saidha S. Calabresi P.A. Prince J.L. Inter-scanner variation independent descriptors for constrained diffeomorphic registration of retinal OCT Proc. SPIE Med. Imaging (SPIE-MI 2018) 2018 (pp. 10574-10574-9) PMC6834339 31695241
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Aresta, G., Araújo, T., Fazekas, B., Mai, J., Schmidt-Erfurth, U., & Bogunović, H. (2024) · IEEE Access · doi:10.1109/access.2024.3379015 · Cited by 5 · see citing works
“The proposed system is evaluated on two public datasets, HC/MS [46] and DME [6] and on an internal dataset consisting of scans of eyes with neovascular AMD (nAMD) (Fig.”
Method
- Authors
- He, Y., Carass, A., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- He, Yufan
- Venue
- Data in Brief
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 22
- Pages
- 601-604
- ISSN
- 2352-3409 (ISSN-L)
- PMC
- PMC6327073
- OpenAlex
- W2907465751
- Cited by
- 90
- References
- 0
- Citations by year
- 2026: 15 · 2025: 14 · 2024: 15 · 2023: 19 · 2022: 15 · 2021: 3 · 2020: 6 · 2019: 3
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Data in Brief (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health
- National Eye Institute (R01-EY024655)
- National Institute of Neurological Disorders and Stroke (R01-NS082347)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Optical Coherence Tomography Applications
- Keywords
- optical coherence tomography, multiple sclerosis, retinal segmentation, manual segmentation, healthy controls, Spectralis OCT, retinal imaging
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