We have made available the world's largest online annotated SD-OCT dataset from 384 intermediate AMD and control subjects utilized in the following paper .

 

S. Farsiu, SJ. Chiu, R.V. O’Connell, F.A. Folgar, E. Yuan, J.A. Izatt, and C.A. Toth

"Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography", 

Ophthalmology, 121(1),  162-172 Jan. (2014).

PDF

Please reference the above paper if you would like to use any part of this method or datasets. 

 

This dataset is assembled in easy to use MATLAB format and is over 20GB in size and is broken into the following compartments. 

Individual SDOCT images and marking: 38400 BScans from 269 AMD patients and 115 normal subjects, their ages, and their corresponding segmentation boundaries on a 5mm diameter centered at the fovea.

NOTE: These results are based on measurements on Bioptigen system. Our preliminary results indicate differences in measured thickness between and even within manufacturers (e.g., a correction factor of 1.16 would be used to convert our reported thicknesses at central fovea to those from Spectralis [Heidelberg Inc, Heidelberg, Germany]). 

Complementary data which includes

1: (269+115= )384 Indiviudal Subject thickness maps for Total retina (TR, between the ILM and the inner aspect of Bruch’s membrane) and their mean and std maps.

2: 384 Indiviudal Subject thickness maps for neurosensory retinal (NSR, from the ILM to the inner aspect of RPE) and their mean and std maps.

3: 384 Indiviudal Subject thickness maps for retinal pigment epithelium and drusen complex (RPEDC, the axial distance from the apex of the drusen and RPE layer to Bruch’s membrane) and their mean and std maps..

Please contact Prof. Sina Farsiu, PhD or Prof. Cynthia A. Toth, MD if you have questions about this dataset.

 


Super
                                                          Resolution
                                                          Sina Farsiu
                                                          Duke
                                                          Superresolution

Vision and Image Processing (VIP) Laboratory 

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