Iowa Reference Algorithms: Human and Murine OCT Retinal Layer Analysis and Display

Human and Murine Retinal OCT Analysis and Display

The Iowa Reference Algorithms are a suite of algorithms for quantitative analysis of human and murine 3-D retinal Optical Coherence Tomography images. 

It was developed by the members of the Retinal Image Analysis Laboratory (see below).

The Iowa Reference Algorithms are compatible with Windows XP, and 7 and above. Mac-OS, Linux, Unix, and other operating systems are currently not supported. The mouse OCT layer segmentation algorithm requires approximately 48GB of RAM. They are compatible with SD-OCT image data from all widely-available clinical OCT scanners, including Heidelberg Spectralis, Topcon 1000 and 2000, Zeiss Cirrus, and Bioptigen. In some cases a research agreement is required from the manufacturer of your OCT scanning device to enable export of raw image data from the OCT scanner, please contact the respective OCT manufacturer if/as needed. Potentially useful, Zeiss provides “research browser” software to convert their custom .dcm to .img, and Heidelberg provides software to convert .e2e to .vol .  

The Iowa Reference Algorithms are for investigational purposes only, and are not approved for clinical care nor by the the US Food and Drug Agency.

Retinal Image Analysis Laboratory

Retinal Image Analysis Laboratory is home to an interdisciplinary group of researchers from the Stephen A. Wynn Insitute for Vision Research, and the Departments of Ophthalmology and Visual Sciences, Electrical and Computer Engineering, Biomedical Engineering, all at the University of Iowa. This group is an integral part of the Iowa Institute for Biomedical Imaging. The Lab has been very active in developing retinal SD-OCT analysis suites for quantitative retinal analysis.

 Iowa Reference Algorithm Stable Releases contain the following:

  • OCTExplorer – for easy viewing and annotations of scans and resulting analyses
  • OCTSegmenter – this module can be called from the OCT-Explorer and segments eleven retinal surfaces in macula-centered OCT volumes. See our publications for more info.
  • Manual

We plan to release software providing additional retinal OCT analysis capabilites in the months and years ahead.

See below for Release Information and Download links.

Citing the Iowa Reference Algorithm

The Iowa Reference Algorithms were developed by Kyungmoo Lee, Michael D. Abramoff, Mona Garvin, and Milan Sonka, as well as other lab members. The underlying n-dimensional graph-based multi-surface segmentation is based on the graph-theoretical and algorithmic work of Xiaodong Wu.

By using the Iowa Reference Algorithms, you are obliged to:

  • Refer to it as “The Iowa Reference Algorithms (Retinal Image Analysis Lab, Iowa Institute for Biomedical Imaging, Iowa City, IA)
  • Reference at least three of the following papers in any publications that use the Iowa Reference Algorithms in any way by choosing the papers that are most relevant to your work.
    • Abramoff MD, Garvin M, Sonka M. Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208, 2010. doi:10.1109/RBME.2010.2084567
    • Kang L, Wu X, Chen DZ, Sonka M. Optimal Surface Segmentation in Volumetric Images – A Graph-Theoretic Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 28:119-134, 2006. doi:10.1109/TPAMI.2006.19
    • Garvin MK, Abramoff MD, Wu X, Burns TK, Russell SR, Sonka M. Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images. IEEE Trans Med. Imaging, 28, 9, 1436-47, 2009. doi:10.1109/TMI.2009.2016958
    • Antony B, Abramoff MD, Tang L, Ramdas WD, Vingerling JR, Jansonius NM, Lee K, Kwon YH, Sonka M, Garvin MK. Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images. Biomed Opt Express 2(8):2403-16, 2011. doi:10.1364/BOE.2.002403
    • Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma. Guo Z, Kwon YH, Lee K, Wang K, Wahle A, Alward WLM, Fingert JH, Bettis DI, Johnson CA, Garvin MK, Sonka M, Abràmoff MD. Invest Ophthalmol Vis Sci. 2017 Aug 1;58(10):3975-3985. doi: 10.1167/iovs.17-21832. PMID: 28796875
    • 4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans. Oguz I, Abramoff MD, Zhang L, Lee K, Zhang EZ, Sonka M. Invest Ophthalmol Vis Sci. 2016 Jul 1;57(9):OCT621-OCT630. doi: 10.1167/iovs.15-18924. PMID: 27936264
    • Quantitative analysis of retinal OCT. Sonka M, Abràmoff MD. Med Image Anal. 2016 Oct;33:165-169. doi: 10.1016/ Epub 2016 Jul 12. PMID: 27503080
    • Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Sohn EH, van Dijk HW, Jiao C, Kok PH, Jeong W, Demirkaya N, Garmager A, Wit F, Kucukevcilioglu M, van Velthoven ME, DeVries JH, Mullins RF, Kuehn MH, Schlingemann RO, Sonka M, Verbraak FD, Abràmoff MD. Proc Natl Acad Sci U S A. 2016 May 10;113(19):E2655-64. doi: 10.1073/pnas.1522014113. Epub 2016 Apr 25. PMID: 27114552
    • Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images. Lee K, Buitendijk GH, Bogunovic H, Springelkamp H, Hofman A, Wahle A, Sonka M, Vingerling JR, Klaver CC, Abràmoff MD. Transl Vis Sci Technol. 2016 Apr 5;5(2):14. eCollection 2016 Mar. PMID: 27066311
    • Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation. Philip AM, Gerendas BS, Zhang L, Faatz H, Podkowinski D, Bogunovic H, Abramoff MD, Hagmann M, Leitner R, Simader C, Sonka M, Waldstein SM, Schmidt-Erfurth U. Br J Ophthalmol. 2016 Oct;100(10):1372-6. doi: 10.1136/bjophthalmol-2015-307985. Epub 2016 Jan 14. PMID: 26769670 
    • Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT. Zhang L, Buitendijk GH, Lee K, Sonka M, Springelkamp H, Hofman A, Vingerling JR, Mullins RF, Klaver CC, Abràmoff MD. Invest Ophthalmol Vis Sci. 2015 May;56(5):3202-11. doi: 10.1167/iovs.14-15669. PMID: 26024104 
    • Relationships of retinal structure and humphrey 24-2 visual field thresholds in patients with glaucoma. Bogunović H, Kwon YH, Rashid A, Lee K, Critser DB, Garvin MK, Sonka M, Abràmoff MD. Invest Ophthalmol Vis Sci. 2014 Dec 9;56(1):259-71. doi: 10.1167/iovs.14-15885. PMID: 25491294 
    • Multi-surface and multi-field co-segmentation of 3-D retinal optical coherence tomography. Bogunovic H, Sonka M, Kwon YH, Kemp P, Abramoff MD, Wu X. IEEE Trans Med Imaging. 2014 Dec;33(12):2242-53. doi: 10.1109/TMI.2014.2336246. Epub 2014 Jul 9. PMID: 25020067 


The current release of Iowa Reference Algorithms is available free of charge for research use. By accepting the licensing agreement, you agree that you will only use it for non-commerical research. Commercial licenses are available; for more information, please contact

Not Free

The Retinal Image Analysis Laboratory has developed and continues developing a growing suite of SD-OCT image analysis software tools and modules. Some of these – after careful validation – will be added to the free research-only section provided for download on this page. Others, however, will only be available to our direct research collaborators with licensing governed by special license agreements between the University of Iowa and the second parties.

Similarly, commercial licenses are available.

Please contact for additional information about software not included in the free release.


The User Manual pdf is also included in the download.


Development was supported, in part, by the following funding sources:

  • NIH-NIBIB R01-EB044640
  • NIH-NEI R01-EY018853
  • NIH-NEI RO1-EY019112
  • Research to Prevent Blindness
  • NIH-NEI EY017066
  • Arnold and Mabel Beckman Initiative for Macular Research

Release Information

Pre-Release 5.0.0 (beta)

Version 5.0.0 (beta) improvements over versions 3.8.0 and 4.0.0 (beta)
  • Improved 11 retinal layer segmentation of macular OCT scans (additional subretinal virtual space)
  • 5.x layer segmentation algorithm including user interaction is fundamentally different from 3.x and 4.x versions.
  • Output of version 5.x is compatible with version 3.8 and later.
  • Nightly validation of segmentation output
  • User manual update
  • Requirements: 64-bit Microsoft Windows or Mac OS X, 6 GB RAM for non-cystic and 20 GB RAM for cystic Zeiss OCT scans (200 x 1024 x 200 voxels)
  • (Does not include OCT Explorer)

Current Release 3.8.0 (stable)

Version 3.8.0 improvements over version 3.6
  • Due to many requests, the 3.x versions will be maintained in the future. Currently this is the stable version.
  • Improved 10 retinal layer segmentation of macular and ONH OCT scans
  • All segmentation parameters have been made device independent for increased robustness across scanners.
  • Aligned coordinate system for data representation with all other versions (switched y- and z- axes from version 3.6)
    • ​Output of version 3.8.0 is compatible with the output from 4.0.0 (beta).
  • Bug fix for the circular/elliptical grid regions starting/ending 90 or 270 degrees
  • User manual update
  • Nightly validation of segmentation output
  • Requirements: Microsoft Windows x64, 4 GB RAM
(History of older releases)


Please fill the information form below to request access to software download links.

This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
18 + 0 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Go to top