Dataset Information

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

ABSTRACT: PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five steps for binary retinal fluid segmentation. The method is based on feature extraction, pre-segmention, dimension reduction procedures, and supervised learning tools.ResultsFluid identification using our pipeline was tested on two separate patient groups: one associated to neovascular age-related macular degeneration, the other showing diabetic macular edema. For training and evaluation purposes, retinal fluid was annotated manually in each cross-section by human expert graders of the Vienna Reading Center. Compared with the manual annotations, our pipeline yields good quantification, visually and in numbers.ConclusionsBy demonstrating good automated retinal fluid quantification, our pipeline appears useful to expert graders within their current grading processes. Owing to dimension reduction, the actual learning part is fast and requires only few training samples. Hence, it is well-suited for integration into actual manufacturer's devices, further improving segmentation by its use in daily clinical life.

SUBMITTER: Breger A

PROVIDER: S-EPMC5584504 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

Breger A A Ehler M M Bogunovic H H Waldstein S M SM Philip A-M AM Schmidt-Erfurth U U Gerendas B S BS

Eye (London, England) 20170421 8

PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five steps for binary retinal fluid segmentation. The method is based on feature extraction, pre-segmention, dimension reduction procedures, and supervised learning tools.ResultsFluid identification using our pipeline was tested on two separate patient group ...[more]

PMID: 28430181

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Project description:Importance:Quantitative measurements based on optical coherence tomographic angiography (OCTA) may have value in managing diabetic retinopathy (DR), but there is limited information on the ability of OCTA to distinguish eyes with DR. Objective:To evaluate the ability of measurements of retinal microvasculature using OCTA to distinguish healthy eyes from eyes with DR. Design, Setting, and Participants:In this prospective cross-sectional study, OCTA was used to examine the eyes of participants with type 2 diabetes with or without DR and the eyes of participants without diabetes from September 17, 2015, to April 6, 2016. Density maps based on superficial retinal layer (SRL) and deeper retinal layer (DRL) images were generated after a method to remove decorrelation tails was applied to the DRL images. Exposures:Both eyes of each participant were examined by means of a 3-mm OCTA scan and 7-field fundus photography using the Diabetic Retinopathy Severity Scale. Main Outcomes and Measures:Two measures were examined: perfusion density, based on the area of vessels, and vessel density, based on a map with vessels of 1-pixel width. The size of the foveal avascular zone was also calculated automatically, and so was the area under the receiver operating characteristic curve. Results:A total of 50 eyes from 26 participants with diabetes (10 women and 16 men; mean [SD] age, 64.9 [7.5] years) and 50 healthy eyes from 25 participants without diabetes (14 women and 11 men; mean [SD] age, 64.0 [7.1] years) were imaged. All participants were white. Vessel density measured in the SRL had the highest area under the receiver operating characteristic curve (0.893 [95% CI, 0.827-0.959]), compared with perfusion density in the SRL (0.794 [95% CI, 0.707-0.881]), foveal avascular zone area (0.472 [95% CI, 0.356-0.588]), and vessel density in the DRL (0.703 [95% CI, 0.601-0.805]). Vessel density in the SRL negatively correlated with best-corrected visual acuity (r?=?-0.28; P?=?.05) and severity of DR (r?=?-0.46; P?=?.001). Density metrics correlated with age. No correlation was detected between vascular density or foveal avascular zone metrics and hemoglobin A1C or duration of diabetes. Conclusions and Relevance:Vessel density measured by OCTA provides a quantitative metric of capillary closure that correlates with severity of DR and may allow staging, diagnosis, and monitoring that do not require subjective evaluation of fundus images.

Dataset Information

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

Publications

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

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