In this study, we used a dataset of 3173 OCT images from healthy and abnormal subjects Images acquired using different OCT devices. We divided these images into six subsets as described in Table I. Subset I consisted of 1568 images from healthy subjects and patients with multiple sclerosis (MS) obtained by the Heidelberg OCT platform (Heidelberg Engineering, Heidelberg, Germany). This subset has been presented in [1]. Subset II is composed of 116 images from abnormal cases (AMD pathology).This dataset has been acquired from the spectral domain (SD-OCT) imaging system from Bioptigen, Inc (Research Triangle Park, NC) and has been utilized in [2]. Subset III is acquired from a Custom-made swept-source OCT (SS-OCT) imaging system designed and built in Department of Biomedical Engineering, University of Basel. This dataset consists of 45 subjects without eye pathologies collected in Didavaran eye clinic, Isfahan, Iran. This subset has been used in [3], [4]. Subset IV has also been used in [5] and contains thirteen 3D macular SD-OCT images obtained from eyes without pathologies using Topcon 3D OCT-1000 imaging system in Ophthalmology Department, Feiz Hospital, Isfahan, Iran. Subset V consists of 63 images with a resolution of 1024x960 pixels obtained at the Farabi Eye Hospital's Retinal Service Center in Tehran, Iran from 30 patients with a variety of eye pathologies. This subset hasbeen used in [6]. Subset VI is studied in [7] and consists of 193 OCT images from 19 DME patients. This dataset has been acquired from the Heidelberg system version 5.1.

** This work was supported in part by the Leading House South Asia and Iran, Zurich University of Applied Sciences (Switzerland), under the Research Seed Money Grant; in part by the National Institute for Medical Research Development (NIMAD) under Grant 976795; and in part by the Vice-Chancellery for Research and Technology, Isfahan University of Medical Sciences, under Grant 298236.

All Dataset and corresponding labels based on RPE layers can be observed in: Link

[1] Y. He, A. Carass, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls,” Data Br., vol. 22, pp. 601–604, 2019.

[2] S. Farsiu et al., “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology, vol. 121, no. 1, pp. 162–172, 2014.

[3] M. Tajmirriahi, R. Kafieh, Z. Amini, and H. Rabbani, “A Lightweight Mimic Convolutional Auto-encoder for Denoising Retinal Optical Coherence Tomography Images,” IEEE Trans. Instrum. Meas., 2021.

[4] M. Tajmirriahi, Z. Amini, A. Hamidi, A. Zam, and H. Rabbani, “Modeling of Retinal Optical Coherence Tomography Based on Stochastic Differential Equations: Application to Denoising,” IEEE Trans. Med. Imaging, vol. 40, no. 8, pp. 2129–2141, 2021, doi: 10.1109/TMI.2021.3073174.

[5] R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal., vol. 17, no. 8, pp. 907–928, 2013.

[6] Roya Arian, Tahereh Mahmoudi, Hamid Riazi-Esfahani, Raheleh Kafieh,Hooshang Faghihi,Ahmad Mirshahi, “Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images Low Contrast Sclerochoroidal Junction Using Deep Learning,” 2021.

[7] M. Montazerin et al., “Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images,” arXiv Prepr. arXiv2003.05916, 2020.

This work is done by Narges Saeedizadeh, Mahnoosh Tajmirriahi, Alireza Haghani, Zahra Amini, Elias Khalili Pour, Hamid Riazi-Esfahani, Kaveh Fadakar, Rahele Kafieh, Hossein Rabbani.

If you find this work or the dataset useful, you can refer our work as:

@article{ title={A Device-independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying TV-Unet for RPE Layer Detection},

author={Narges Saeedizadeh, Mahnoosh Tajmirriahi, Alireza Haghani, Zahra Amini, Elias Khalili Pour, Hamid Riazi-Esfahani, Kaveh Fadakar, Rahele Kafieh, Hossein Rabbani},

journal={IEEE Transactions on Instrumentation & Measurement},

year={2022} }