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bioRxiv · 10.1101/135640

Deep-Learning Based, Automated Segmentation Of Macular Edema In Optical Coherence Tomography

Lee, C. S.·Tyring, A. J.·Deruyter, N. P.·Wu, Y.·Rokem, A.·Lee, A. Y.
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Abstract

Evaluation of clinical images is essential for diagnosis in many specialties and the development of computer vision algorithms to analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts. Additionally, the agreement between experts and between experts and CNN were similar. Our results reveal that CNN can be trained to perform automated segmentations.

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Lee, C. S., Tyring, A. J., Deruyter, N. P., Wu, Y., Rokem, A., Lee, A. Y.. 2017-05-09. Deep-Learning Based, Automated Segmentation Of Macular Edema In Optical Coherence Tomography. https://doi.org/10.1101/135640

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