Software for ophthalmic OCT by Voxeleron

An early collaboration with Johns Hopkins School of Medicine showed very promising results

The Septem algorithm for ophthalmic optical coherence tomography (OCT) from Voxeleron LLC (Pleasanton, CA) enables automatic segmentation of seven retinal layers in OCT volumes. The algorithm is based on the company's proprietary denoising methods, as noise in OCT data is a limitation to accurate and repeatable analyses. Applicable to both high- and low-resolution OCT scans, the software works with many available OCT systems. For an OCT volume comprised of 67 million points, the algorithm performs seven-layer segmentation in 3 s on an off-the-shelf PC.



Voxeleron Announces Release of a New Standard in OCT Retinal Segmentation for Ophthalmic OCT Vendors

The Septem algorithm accurately segments seven retinal layers in seconds

Pleasanton, CA  – October 6, 2012 – Voxeleron LLC announces the immediate availability of vendor independent software for the automatic segmentation of seven retinal layers in optical coherence tomography (OCT) volumes.  The Septem algorithm is based on Voxeleron’s proprietary denoising methods, applicable to both high and low-resolution OCT scans.

Jonathan Oakley, Co-Founder and Principal Scientist, says that their methods facilitate faster and more robust analysis of existing volumetric scan patterns and pave the way for lower-cost devices to offer the same analyses as the high-end devices of today.

“Noise in OCT data is an inherent barrier to accurate and repeatable analyses.  We found that by being able to more accurately resolve the true signal in the data, we were able to segment more layers and do so with higher accuracy and even better repeatability.  It sounds obvious, but we only truly saw this by focusing our efforts on scans of lower signal quality or scans with disruptive pathologies.  The ability to segment seven retinal layers both reliably, quickly, and over a wide range of signal conditions makes the algorithm truly unique.  We have seen significantly lower error rates in comparison to commercially available inner retina segmentation algorithms, where fewer layers are segmented.  To achieve this level of performance requires years of development time.  We were only able to do so based on our extensive experience in the development and release of image processing algorithms for ophthalmic OCT.”

“The development of retinal segmentation software that works across many OCT platforms is a major advance for the field,” says Dr. Peter Calabresi, Professor of Neurology at Johns Hopkins.  “The quantitative measures of the inner and outer nuclear layers appear to have predictive value for estimating clinical disability in multiple sclerosis.”

Robert Chang, M.D., Assistant Professor of Ophthalmology at the Stanford School of Medicine, provided additional clinical input and scan data to support the development of the algorithm.

“Based on an early study of their results, I was encouraged to see the Voxeleron algorithm accurately segment layers on scans where a commercially available algorithm had failed,” says Dr. Chang. “With the added ability to segment more retinal layers, I am interested in looking at new parameters for disease. We looked at numerous eyes and saw consistent results, and we plan to publish our findings.”

In the ophthalmic market, as demand for lower-cost, smaller, and more mobile imaging devices increases, image quality will inevitably be compromised.  The Septem algorithm is ideally suited to maintain the accuracy of quantification methods in the face of lower quality images.  Conversely, as the data rates of high-end devices increases, fast and reliable algorithms become even more essential.

For an OCT volume comprised of 67 million points, Voxeleron’s Septem algorithm performs seven layer segmentation in just 3 seconds on an off-the-shelf PC.  Inquiries regarding the licensing of this software can be directed to and additional information can be found at

About Voxeleron
Voxeleron is a Silicon Valley-based software house specializing in the development and licensing of computer vision and machine learning software for industrial and medical applications.  More at

Dr. Peter Calabresi and Dr. Robert Chang have no financial interest in Voxeleron LLC.


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