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Published VersionCC BY V4.0, Open Access
Journal article
Automatic choroidal segmentation in OCT images using supervised deep learning methods
Scientific Reports, Vol.9(1), pp.1-13
2019
PMCID: PMC6746702
PMID: 31527630
Abstract
The analysis of the choroid in the eye is crucial for our understanding of a range of ocular diseases and physiological processes. Optical coherence tomography (OCT) imaging provides the ability to capture highly detailed cross-sectional images of the choroid yet only a very limited number of commercial OCT instruments provide methods for automatic segmentation of choroidal tissue. Manual annotation of the choroidal boundaries is often performed but this is impractical due to the lengthy time taken to analyse large volumes of images. Therefore, there is a pressing need for reliable and accurate methods to automatically segment choroidal tissue boundaries in OCT images. In this work, a variety of patch-based and fully-convolutional deep learning methods are proposed to accurately determine the location of the choroidal boundaries of interest. The effect of network architecture, patch-size and contrast enhancement methods was tested to better understand the optimal architecture and approach to maximize performance. The results are compared with manual boundary segmentation used as a ground-truth, as well as with a standard image analysis technique. Results of total retinal layer segmentation are also presented for comparison purposes. The findings presented here demonstrate the benefit of deep learning methods for segmentation of the chorio-retinal boundary analysis in OCT images.
Details
- Title
- Automatic choroidal segmentation in OCT images using supervised deep learning methods
- Authors
- Jason Kugelman (Author) - Queensland University of TechnologyDavid Alonso-Caneiro (Corresponding Author) - Queensland University of TechnologyScott A. Read (Author) - Queensland University of TechnologyJared Hamwood (Author) - Queensland University of TechnologyStephen J. Vincent (Author) - Queensland University of TechnologyFred K. Chen (Author) - The University of Western AustraliaMichael J. Collins (Author) - Queensland University of Technology
- Publication details
- Scientific Reports, Vol.9(1), pp.1-13
- Publisher
- Nature Publishing Group
- Date published
- 2019
- DOI
- 10.1038/s41598-019-49816-4
- ISSN
- 2045-2322
- PMID
- 31527630; PMC6746702
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Grant note
- APP1142962 / NHMRC Career Development Fellowship National Health and Medical Research Council (NHMRC) of Australia Telethon - Perth Children's Hospital Research Fund Rebecca L. Cooper 2018 Project Grant
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99973598402621
- Output Type
- Journal article
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- Ophthalmology
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