Stratified Sampling Voxel Classification for Segmentation of Intraretinal and Subretinal Fluid in Longitudinal Clinical OCT Data

IEEE Transactions on Medical Imaging
Xiayu XuMichael D Abràmoff

Abstract

Automated three-dimensional retinal fluid (named symptomatic exudate-associated derangements, SEAD) segmentation in 3D OCT volumes is of high interest in the improved management of neovascular Age Related Macular Degeneration (AMD). SEAD segmentation plays an important role in the treatment of neovascular AMD, but accurate segmentation is challenging because of the large diversity of SEAD size, location, and shape. Here a novel voxel classification based approach using a layer-dependent stratified sampling strategy was developed to address the class imbalance problem in SEAD detection. The method was validated on a set of 30 longitudinal 3D OCT scans from 10 patients who underwent anti-VEGF treatment. Two retinal specialists manually delineated all intraretinal and subretinal fluid. Leave-one-patient-out evaluation resulted in a true positive rate and true negative rate of 96% and 0.16% respectively. This method showed promise for image guided therapy of neovascular AMD treatment.

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Citations

Aug 27, 2015·Progress in Retinal and Eye Research·Ursula Schmidt-Erfurth, Sebastian M Waldstein
May 22, 2016·Ophthalmology·Usha ChakravarthyAnat Loewenstein
Feb 12, 2017·IEEE Transactions on Medical Imaging·Jelena NovoselLucas J van Vliet
Jul 31, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Dehui XiangXinjian Chen
Jun 20, 2018·Graefe's Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Für Klinische Und Experimentelle Ophthalmologie·Claus von der BurchardJohann Roider
Oct 16, 2018·Biomedical Optics Express·Plácido L VidalMarcos Ortega
Dec 29, 2020·American Journal of Ophthalmology·Tiarnan D L KeenanUrsula Schmidt-Erfurth
Jun 4, 2021·Journal of Digital Imaging·Loza Bekalo SappaQiang Chen

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