Conference proceeding
Optimal retinal cyst segmentation from OCT images
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol.9784, pp.97841E-97841E-7
03/21/2016
DOI: 10.1117/12.2217355
Abstract
Accurate and reproducible segmentation of cysts and fluid-filled regions from retinal OCT images is an important step allowing quantification of the disease status, longitudinal disease progression, and response to therapy in wet-pathology retinal diseases. However, segmentation of fluid-filled regions from OCT images is a challenging task due to their inhomogeneous appearance, the unpredictability of their number, size and location, as well as the intensity profile similarity between such regions and certain healthy tissue types. While machine learning techniques can be beneficial for this task, they require large training datasets and are often over-fitted to the appearance models of specific scanner vendors. We propose a knowledge-based approach that leverages a carefully designed cost function and graph-based segmentation techniques to provide a vendor-independent solution to this problem. We illustrate the results of this approach on two publicly available datasets with a variety of scanner vendors and retinal disease status. Compared to a previous machine-learning based approach, the volume similarity error was dramatically reduced from 81:3±56:4% to 22:2±21:3% (paired t-test, p << 0:001).
Details
- Title: Subtitle
- Optimal retinal cyst segmentation from OCT images
- Creators
- Ipek Oguz - The Univ. of Iowa (United States)Li Zhang - The Univ. of Iowa (United States)Michael D Abràmoff - The Univ. of Iowa (United States)Milan Sonka - The Univ. of Iowa (United States)
- Contributors
- Martin A Styner (Editor) - The Univ. of North Carolina at Chapel Hill (United States)Elsa D Angelini (Editor) - Columbia Univ. (United States)
- Resource Type
- Conference proceeding
- Publication Details
- Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol.9784, pp.97841E-97841E-7
- DOI
- 10.1117/12.2217355
- ISSN
- 1605-7422
- Publisher
- SPIE
- Language
- English
- Date published
- 03/21/2016
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984060751502771
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