DISCLAIMER: “All images included in this website have been fully de-identified. Any dates associated with the imaging files do not relate to the subject or to date of image acquisition. Images are intended for use in research and educational settings. Commercialization/redistribution of the images is prohibited. In the unlikely event that you identify any remaining identifiers in the images, you are prohibited from further disclosure and should destroy all copies of the image and immediately notify the owner of this webpage at: sina.farsiu@duke.edu All use of the images should include citation and credit to their corresponding paper.”
Data Sets:
Through the years, according to the reproducible research principal, me and my colleagues have made available almost all data sets used in our publications. The condition for using any of these data-sets in your research is referencing our corresponding publication.
1: World's largest annotated SD-OCT dataset for intermediate AMD and control subjects used in our recent paper
Individual SDOCT images and marking: 35400 BScans from 269 AMD patients and 115 normal subjects, their ages, and their corresponding segmentation boundaries on a 5mm diameter centered at the fovea are available here.
2: Dataset for Denoising and interpolation Ophthalmic SDOCT Images (human and mice),
Raw data as well as dataset with results from our sparsity based novel sparsity based simultaneous denoising and interpolation (SBSDI) method (TMI, 2013) as compared to other state of the art denoising algorithms are available here.
3: Dataset for Denoising Ophthalmic SDOCT Images
Raw data as well as dataset with results from our sparsity based novel multiscale sparsity based tomographic denoising (MSBTD) method (BOE, 2012) as compared to other state of the art denoising algorithms are available here
4: Dataset for Classification of Ophthalmic SD-OCT Images of Normal, diabetic macular edema, and dry age-related macular degeneration subjects
This dataset consists of volumetric scans acquired from 45 patients: 15 normal patients, 15 patients with dry AMD, and 15 patients with DME using Spectralis SD-OCT (Heidelberg Engineering Inc.) are available here
5: Dataset for Superresolution and demosaicing
Most data sets that I have created for different super-resolution and demosaicing scenarios are available here.
6: Dataset for SD-OCT Retinal Image Analysis
Dataset, with automatic and manual annotations used in our recent automated retinal image analysis paper are available here.
7: Dataset for Kernel Regression Kernel Regression Image Processing
8: Dataset for Confocal Microscopy RPE Cell Analysis
Dataset, with automatic and manual annotations used in our recent automated closed contour feature segmentation paper are available here.
9: Dataset for AO-SLO cone photoreceptor automatic segmentation and Analysis
Dataset, with automatic and manual annotations used in our recent automated AOSLO cone segmentation paper are available here.
10: Dataset for FA leakage segmentation and Analysis in DME eyes
Dataset, with manual annotations used in our recent automated segmentation of fluorescein leakage in subjects with diabetic macular edema paper are available here.
11: Dataset for OCT cyst and layer segmentation and Analysis in DME eyes
Dataset, with manual annotations used in our recent automated segmentation of OCT images in subjects with diabetic macular edema paper are available here.
12: Dataset for Artery/Vein Classification
Dataset, with manual annotations used in our recent paper on artery/vein classification on wide field-of-view (as well as classic small field of view) retinal images are available here.
13: Dataset for Tree Topology Estimation
Dataset, with manual annotations used in our recent paper on estimation of 3-D topology from a single 2-D image of wide firld-of view color images of retina, images of rice roots, and synthetic trees are available here.
14: Dataset for Compressed Wavefront Sensing
Dataset used in our recent paper on fast and efficient wavefront sensing are available here.
15: Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks: Annotated dataset for detecting cones on both confocal and AOSLO images are available on GitHub (click on the link). This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2017 by Duke University
16. Dataset for our paper "Fast and robust active neuron segmentation in two-photon calcium imaging using spatio-temporal deep-learning" in PNAS, 2019.
17. Dataset for our paper "Weakly supervised individual ganglion cell segmentation from adaptive optics OCT images for glaucomatous damage assessment" in Optica, 2021.
18. Dataset for our paper " Connectivity-based Deep Learning Approach for Segmentation of the Epithelium in In Vivo Human Esophageal OCT Images" in BOE, 2021.
19. Dataset for our paper " Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eye" in BOE, 2023.
20. Dataset for our paper " " Open-Source Automated Segmentation of Neuronal Structures in Corneal Confocal Microscopy Images of the Subbasal Nerve Plexus with Accuracy on Par with Human Grading" in Cornea, 2023.
Software:
1. Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks: Source Code for our recent paper on detecting cones on both confocal and AOSLO images are available on GitHub (click on the link). This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2017 by Duke University
3. Automated Vessel Segmentation on Video Indirect Ophthalmology Software: Source Code for our recent paper are available here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2012 by Duke University
4. Sparsity based simultaneous denoising and interpolation (SBSDI) :Software and dataset for our recent paper are available here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2013 by Duke University
5. Sparse Zernike Representation (SPARZER) for Wavefront Sensing: Software and dataset for our recent paper are available here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2014 by Duke University
6. Multiscale Sparsity Based Tomographic Denoising (MSBTD): Software and dataset for our recent paper are available here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2012 by Duke University
7. MDSP Resolution Enhancement (Super Resolution) Software: According to the reproducible research principal, I created this (now commercialized) software package in collaboration with Drs. Dirk Robinson, Prof. Michael Elad, and Prof. Peyman Milanfar while at UCSC. The main function of this software tool is the implementation of several superresolution techniques in our published work, and the comparison of these to other work. The techniques implemented cover robust methods, dynamic color super-resolution methods, and simultaneous demosaicing and resolution enhancement. Please forward all your questions and enquiries directly to my former PhD advisor Prof. Milanfar, who is managing/distributing the software. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2004 by University of California.
Also, due to its popularity, many other teams have also implemented our algorithms and have distributed open access code online. Here is an example implementation of our core method (Farsiu et al. IEEE TIP 2004) by an external investigator.
8. Adjusted Mean Arc Length (AMAL) Graph Search source code: Get the source code from here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2016 by Duke University
9. Kernel Regression code: Get the Kernel Regression Image Processing Toolbox and dataset from here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2007 by University of California
10. Segmentation based sparse reconstruction (SSR) :Software for our recent paper are available here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution.
11. Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes : Source code for our recent paper are available here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution.
12. Software for color based segmentation of mullar glia cells : Source code for our recent paper are available here. This is experimental software. It is provided for non-commercial research purposes only. Use at your own risk. No warranty is implied by this distribution.
13. Open source software for our paper ""Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography, IEEE TMI, 2018"
14. Open source software for our paper "Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging"
15. Open source software for our paper "Fast and robust active neuron segmentation in two-photon calcium imaging using spatio-temporal deep-learning" in PNAS 2019.
16. Open source software for our paper "Optical coherence refraction tomography " in Nature Photonics 2019.
17. Open source software for our paper on " automatic segmentation of ocular structures and biomarkers of microbial keratitis on slit-lamp photographyimages " .
18. Open source software for our CVPR paper on "mesoscopic photogrammetry with an unstabilized phone camera " .
19. Open source software for our Pattern Recognition paper on "BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection " .
20. Open source software for our TVST paper on "Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis " .
21. Open source software for our Experimental Eye Research paper on "Open-Source Deep Learning-Based Automatic Segmentation of Mouse Schlemm’s Canal in Optical Coherence Tomography Images " .
22. Open source software and dataset for our Optica paper on " 3D OCRT " .
23. Open source software for our IEEE TMI paper " RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation " .
24. Code for our CVPR paper " Directional Connectivity-based Segmentation of Medical Images " .
25. Code for our paper " " Open-Source Automated Segmentation of Neuronal Structures in Corneal Confocal Microscopy Images of the Subbasal Nerve Plexus with Accuracy on Par with Human Grading" in Cornea, 2023.