Device-independent 8 retinal layer segmentation software.
Voxeleron is pleased to offer Orion, advanced optical coherence tomography (OCT) analysis software for research use only*. Orion is fully-automated software, rapidly and accurately segmenting up to eight retinal layers in OCT images.
The limited functionality of existing OCT analysis tools has meant that only two or three retinal layers are analyzed, despite the overwhelming evidence for the clinical importance of the inner retinal layers in disease detection and prognosis. Orion addresses exactly this technology gap, providing researchers much needed biomarkers for the early detection and prognosis of ocular and neurological diseases.
Previously voted as one of the year’s best innovations in the Ophthalmologist, Orion is a fully automated research tool designed to rapidly and accurately segment eight retinal layers in OCT images. The software is platform independent, processing data from all the main OCT scanners, and provides a comprehensive tool for quantifying retinal thickness in the human retina. Orion has been validated in two separate studies, and thoroughly beta tested at a number of clinical sites around the world.
As well as unrivaled image format support, Orion has a comprehensive set of features, including 3d visualization, layer editing wizards, angiography support, batch processing, and region of interest (ROI) analysis. Image data and results can be saved to standard DICOM for integration in existing IT infrastructures, facilitating the ability to standardize ophthalmic formats used in mulit-center trials.
*Please note: Orion has not been approved for clinical use. It is a research tool, and may not be used to support the treatment or diagnosis of human patients.
A window to the brain
Five million Americans suffer from Alzheimer’s disease, one million from Parkinson’s, 30,000 from amyotrophic lateral sclerosis (ALS), and it is estimated that 400,000 (1999) Americans are taken early from the workforce due to multiple sclerosis (MS), a disease whose onset afflicts younger populations. As an extension of the central nervous system, the health of the retina is closely tied to the health of the brain. Early work studying deeper layers of the retina has shown potential for early detection and accurate diagnosis or some of the most debilitating neurological diseases. A number of recent studies have tied retinal layer thicknesses to an increasing number of pathologies, including:
Early detection of these diseases can offer a number of potential interventional options and clinical endpoints. Orion is unique in offering the ability to isolate and quantify these layers, empowering fundamental clinical research. For further details please email us at email@example.com, or to see Orion in action, visit our video tutorials webpage.
Future releases are coming as we have an extensive product roadmap! We introduced a deep-learning based approach to B-scan abnormality detection at 2017’s ARVO and added more widespread support for angiography quantification at 2019’s ARVO meeting as well as deep learning-based segmentation of the choroid (see this link for details). 2018 saw the release of a comprehensive suite of change analysis tools, which features fully automated alignment (snapshot of the analysis page is shown above), a project that was fully supported by the NIH in the form of an SBIR grant. In addition to that, Orion offers a number of significant workflow advantages that are a boon to clinical research. Indeed, no other software offering comes close to matching the quality or breadth of the features delivered in Orion, so regardless of the OCT instrument you use for your research (as we support them all), do get in touch to request a demonstration or a trial version of the software. And did we mention that 2020 will see yet more deep learning-based methods, including fluid segmentation and quantification (example figure below)?
Watch this space for further updates (or even easier, just sign up below)!
Orion Information List Signup
- Marziani E. et al. Evaluation of retinal nerve fiber layer and ganglion cell layer thickness in Alzheimer’s disease using spectral-domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. August 6, 2013.
- Chang et al., Program No. 710.13, Society for Neuroscience, San Diego, 2013.
- Philipp Albrecht, Ann-Kristin Müller, Martin Südmeyer, et al. Optical Coherence Tomography in Parkinsonian Syndromes. PLoS One. 2012; 7(4): e34891. Published online Apr 13, 2012.
- Garcia-Martin E et al. Distribution of retinal layer atrophy in patients with Parkinson disease and association with disease severity and duration. Am J Ophthalmol. 2014 Feb;157(2):470-478.
- Bayhan et al., Current Eye Research, 2014.
- Schneider et al., Journal of Neural Transmission, January 2014, Volume 121, Issue 1, pp 41-47
- Ringelstein M. et al. Subtle retinal pathology in amyotrophic lateral sclerosis. Annals of Clinical and Translational Neurology, Volume 1, Issue 4, pages 290-297, April 2014.
- Saidha S., et al. Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study. The Lancet Neurology, Volume 11, Issue 11, Pages 963 – 972, November 2012.
- Saidha S, Syc SB, Ibrahim MA, Eckstein C, Warner CV, Farrell SK, Oakley JD, Durbin MK, Meyer SA, Balcer LJ, Frohman EM, Rosenzweig JM, Newsome SD, Ratchford JN, Nguyen QD, Calabresi PA. Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography. Brain. 2011 Feb;134(Pt 2):518-33.
- Green A. Getting beyond the ganglion cell: morphometric adjustments for retinal optical coherence tomography in multiple sclerosis. JAMA Neurol. 2013 Jan;70(1):13-5.
- Saidha S, Sotirchos ES, Oh J, Syc SB, Seigo MA, Shiee N, Eckstein C, Durbin MK, Oakley JD, Meyer SA, Frohman TC, Newsome S, Ratchford JN, Balcer LJ, Pham DL, Crainiceanu CM, Frohman EM, Reich DS, Calabresi PA. Relationships between retinal axonal and neuronal measures and global central nervous system pathology in multiple sclerosis. JAMA Neurol. 2013 Jan;70(1):34-43.