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

Xiayu Xu; Kyungmoo Lee; Li Zhang; Milan Sonka; Michael D Abràmoff

doi:10.1109/TMI.2015.2408632

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

Journal article

Peer reviewed

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

Xiayu Xu, Kyungmoo Lee, Li Zhang, Milan Sonka and Michael D Abràmoff

IEEE transactions on medical imaging, Vol.34(7), pp.1616-1623

07/2015

DOI: 10.1109/TMI.2015.2408632

PMID: 25769146

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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.

subretinal fluid

class imbalance

intraretinal fluid

stratified sampling

Age-related macular degeneration

Details

Title: Subtitle: Stratified Sampling Voxel Classification for Segmentation of Intraretinal and Subretinal Fluid in Longitudinal Clinical OCT Data
Creators: Xiayu Xu - University of Iowa
Kyungmoo Lee - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA
Li Zhang - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA
Milan Sonka - Department of Electrical and Computer Engineering, the Department of Ophthalmology and Visual Sciences, and the Department of Radiation Oncology, the University of Iowa, Iowa City, IA 52242 USA
Michael D Abràmoff - Department of Ophthalmology and Visual Sciences, the Department of Electrical and Computer Engineering, the Department of Biomedical Engineering, the University of Iowa, Iowa City, IA 52242 USA, and also with the VA Medical Center, Iowa City, IA 52246 USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.34(7), pp.1616-1623
DOI: 10.1109/TMI.2015.2408632
PMID: 25769146
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 81401480
Language: English
Date published: 07/2015
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9983806396802771

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