Journal article
Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images
Biomedical optics express, Vol.9(9), pp.4509-4526
09/01/2018
DOI: 10.1364/BOE.9.004509
PMID: 30615698
Abstract
Automated segmentation of object boundaries or surfaces is crucial for quantitative image analysis in numerous biomedical applications. For example, retinal surfaces in optical coherence tomography (OCT) images play a vital role in the diagnosis and management of retinal diseases. Recently, graph based surface segmentation and contour modeling have been developed and optimized for various surface segmentation tasks. These methods require expertly designed, application specific transforms, including cost functions, constraints and model parameters. However, deep learning based methods are able to directly learn the model and features from training data. In this paper, we propose a convolutional neural network (CNN) based framework to segment multiple surfaces simultaneously. We demonstrate the application of the proposed method by training a single CNN to segment three retinal surfaces in two types of OCT images - normal retinas and retinas affected by intermediate age-related macular degeneration (AMD). The trained network directly infers the segmentations for each B-scan in one pass. The proposed method was validated on 50 retinal OCT volumes (3000 B-scans) including 25 normal and 25 intermediate AMD subjects. Our experiment demonstrated statistically significant improvement of segmentation accuracy compared to the optimal surface segmentation method with convex priors (OSCS) and two deep learning based UNET methods for both types of data. The average computation time for segmenting an entire OCT volume (consisting of 60 B-scans each) for the proposed method was 12.3 seconds, demonstrating low computation costs and higher performance compared to the graph based optimal surface segmentation and UNET based methods.
Details
- Title: Subtitle
- Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images
- Creators
- Abhay Shah - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USALeixin Zhou - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USAMichael D Abràmoff - Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, IA, USAXiaodong Wu - Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- Biomedical optics express, Vol.9(9), pp.4509-4526
- Publisher
- United States
- DOI
- 10.1364/BOE.9.004509
- PMID
- 30615698
- ISSN
- 2156-7085
- eISSN
- 2156-7085
- Language
- English
- Date published
- 09/01/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984060792502771
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