Automatic assessment of time-resolved OCT images for selective retina therapy

Zbinden, Sarah; Steiner, Patrick; Kucur, Serife Seda; Wolf, Sebastian; Sznitman, Raphael (2016). Automatic assessment of time-resolved OCT images for selective retina therapy. International Journal of Computer Assisted Radiology and Surgery, 11(6), pp. 863-871. Springer 10.1007/s11548-016-1383-6

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In recent years, selective retina laser treatment (SRT), a sub-threshold therapy method, avoids widespread damage to all retinal layers by targeting only a few. While these methods facilitate faster healing, their lack of visual feedback during treatment represents a considerable shortcoming as induced lesions remain invisible with conventional imaging and make clinical use challenging. To overcome this, we present a new strategy to provide location-specific and contact-free automatic feedback of SRT laser applications.

We leverage time-resolved optical coherence tomography (OCT) to provide informative feedback to clinicians on outcomes of location-specific treatment. By coupling an OCT system to SRT treatment laser, we visualize structural changes in the retinal layers as they occur via time-resolved depth images. We then propose a novel strategy for automatic assessment of such time-resolved OCT images. To achieve this, we introduce novel image features for this task that when combined with standard machine learning classifiers yield excellent treatment outcome classification capabilities.

Our approach was evaluated on both ex vivo porcine eyes and human patients in a clinical setting, yielding performances above 95 % accuracy for predicting patient treatment outcomes. In addition, we show that accurate outcomes for human patients can be estimated even when our method is trained using only ex vivo porcine data.

The proposed technique presents a much needed strategy toward noninvasive, safe, reliable, and repeatable SRT applications. These results are encouraging for the broader use of new treatment options for neovascularization-based retinal pathologies.

Item Type:

Journal Article (Original Article)


10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Steiner, Patrick, Kucur, Serife Seda, Wolf, Sebastian (B), Sznitman, Raphael


500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering








Raphael Sznitman

Date Deposited:

08 Jun 2016 12:49

Last Modified:

05 Dec 2022 14:55

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

Computer-assisted intervention; Feature design; Retinal laser therapy; Time-resolved OCT; Treatment outcome estimation




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