SS-OCT Data

The central wavelength, spectral bandwidth and A-scan rate of the custom-made SS-OCT are 1064 nm, 100 nm, and 100 kHz, respectively.

The participants included people with DME or “1” (73 subjects), Healthy or “0” (54 subjects), and Non-diabetic patients or “2” (64 subjects) with AMD, CNV, or Macular Hole (MH) disorders. Each subject contains 300 B-scans with 300 to 1200 x 300 pixels resolution. These 191 are available in “1-Raw Dataset”.

Since some of the data have poor quality or the macular region was not captured, Quality Assessor (QA) was designed to automatically determine if the B-scan is suitable for further analyses or not. For the data preparation step, the manual quality assessment was performed on 191 volumes. The data was divided into two categories: 190 folders of qualified B-scans and 188 folders of non-qualified B-scans. Each folder consists of 1 to 300 B-scans. And all B-scans in each folder belong to one specific subject. The training data for QA is available in “0-QA-data”. Deep Convolutional Neural Network 1 method is employed to train QA. In the next step, QA was used to assess the quality of subjects in “1-Raw Dataset”. Volumes with more than 30 qualified B-scans passed.

0-QA-data: This data includes 190 folders of qualified B-scans and 188 folders of non-qualified B-scans. Each folder consists of 1 to 300 B-scans with 300 x 300 pixels resolution.

1-Raw Dataset: This dataset includes all 191 volumes before any processing. Each volume contains 300 B-scans with 300 to 1200 x 300 pixels resolution. The participants included people with DME or “1” (73 subjects), Healthy or “0” (54 subjects), and Non-diabetic patients or “2” (64 subjects) with AMD, CNV, or Macular Hole (MH) disorders.

2-Raw Dataset QA: Includes 124 volumes which has been obtained after image quality assessment with automatic cropping. Each subject contains 30 to 300 B-scan with 300 x 300 pixels resolution. The participants included people with DME or “1” (40 subjects), Healthy or “0” (50 subjects), and Non-diabetic patients or “2” (34 subjects) with AMD, CNV, or Macular Hole (MH) disorders.

1- J. Kim, A. D. Nguyen, and S. Lee, “Deep CNN-Based Blind Image Quality Predictor,” IEEE Trans. Neural Networks Learn. Syst., vol. 30, no. 1, pp. 11–24, 2018.

2- Z. Amini, and H. Rabbani, “Optical coherence tomography image denoising using Gaussianization transform,” J. Biomed. Opt., vol. 22, no. 8, 2017.

3- 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," in IEEE Transactions on Medical Imaging, vol. 40, no. 8, pp. 2129-2141, Aug. 2021

4- M. Tajmirriahi, R. Rostamian, Z. Amini, A. Hamidi, A. Zam and H. Rabbani, "Stochastic Differential Equations for Automatic Quality Control of Retinal Optical Coherence Tomography images," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 3870-3873, doi: 10.1109/EMBC48229.2022.9870918.

5- Z. Amini and H. Rabbani, "Statistical Modeling of Retinal Optical Coherence Tomography," in IEEE Transactions on Medical Imaging, vol. 35, no. 6, pp. 1544-1554, June 2016, doi: 10.1109/TMI.2016.2519439.

6- Amini Z, Rabbani H. Optical coherence tomography image denoising using Gaussianization transform. Journal of Biomedical Optics. 2017 Aug 1;22(8):086011-.

7- Kafieh R, Rabbani H, Abramoff MD, Sonka M. Curvature correction of retinal OCTs using graph-based geometry detection. Physics in Medicine & Biology. 2013 Apr 11;58(9):2925.