Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning |
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| Authors: | Zhuo Wang Acner Camino Ahmed M Hagag Jie Wang Richard G Weleber Paul Yang Mark E Pennesi David Huang Dengwang Li Yali Jia |
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| Institution: | 1. Casey Eye Institute, Oregon Health and Science University, Portland, Oregon;2. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China |
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| Abstract: | Optical coherence tomography (OCT) can demonstrate early deterioration of the photoreceptor integrity caused by inherited retinal degeneration diseases (IRDs). A machine learning method based on random forests was developed to automatically detect continuous areas of preserved ellipsoid zone structure (an easily recognizable part of the photoreceptors on OCT) in 16 eyes of patients with choroideremia (a type of IRD). Pseudopodial extensions protruding from the preserved ellipsoid zone areas are detected separately by a local active contour routine. The algorithm is implemented on en face images with minimum segmentation requirements, only needing delineation of the Bruch's membrane, thus evading the inaccuracies and technical challenges associated with automatic segmentation of the ellipsoid zone in eyes with severe retinal degeneration.  |
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| Keywords: | choroideremia ellipsoid zone image reconstruction machine learning medical and biomedical imaging ophthalmology optical coherence tomography photoreceptor |
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