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

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      bioRxiv

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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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          Author and article information

          Journal
          bioRxiv
          May 09 2017
          Article
          10.1101/135640
          bd06876b-1546-43ff-bd6d-e858ea5adb45
          © 2017
          History

          Quantitative & Systems biology,Biophysics
          Quantitative & Systems biology, Biophysics

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