Segmentation of retinal low-cost optical coherence tomography images using deep learning

Timo Kepp; Helge Sudkamp; Claus von der Burchard; Hendrik Schenke; Peter Koch; Gereon Hüttmann; Johann Roider; Mattias P. Heinrich; Heinz Handels

doi:10.1117/12.2551324

16 March 2020 Segmentation of retinal low-cost optical coherence tomography images using deep learning

Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias P. Heinrich, Heinz Handels

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113141O (2020) https://doi.org/10.1117/12.2551324
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence or change of disease-specific OCTbased biomarkers. Therefore, the monitoring frequency has a significant influence on the success of AMD therapy. However, the monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. While a higher monitoring frequency would have a positive effect on the success of treatment, in practice it can only be achieved with a home monitoring solution. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes using specific OCT-based biomarkers. In this paper, for the first time, retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT) are segmented using a deep learningbased approach. A convolutional neural network (CNN) is utilized to segment the total retina as well as pigment epithelial detachments (PED). It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging. In addition, a convolutional denoising autoencoder (CDAE) refines the CNN prediction, which has previously learned retinal shape information. It is shown that the CDAE refinement can correct segmentation errors caused by artifacts in the OCT image.

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Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias P. Heinrich, and Heinz Handels "Segmentation of retinal low-cost optical coherence tomography images using deep learning", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141O (16 March 2020); https://doi.org/10.1117/12.2551324

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