Abstract
We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
| Original language | English |
|---|---|
| Pages (from-to) | 2732-2744 |
| Number of pages | 13 |
| Journal | Biomedical Optics Express |
| Volume | 8 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1 May 2017 |
| Externally published | Yes |
Keywords
- Image analysis
- Image processing
- Optical coherence tomography
Cite this
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Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. / Fang, Leyuan; Cunefare, David; Wang, Chong; Guymer, Robyn H.; Li, Shutao; Farsiu, Sina.
In: Biomedical Optics Express, Vol. 8, No. 5, 01.05.2017, p. 2732-2744.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search
AU - Fang, Leyuan
AU - Cunefare, David
AU - Wang, Chong
AU - Guymer, Robyn H.
AU - Li, Shutao
AU - Farsiu, Sina
PY - 2017/5/1
Y1 - 2017/5/1
N2 - We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
AB - We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
KW - Image analysis
KW - Image processing
KW - Optical coherence tomography
UR - http://www.scopus.com/inward/record.url?scp=85019034945&partnerID=8YFLogxK
U2 - 10.1364/BOE.8.002732
DO - 10.1364/BOE.8.002732
M3 - Article
VL - 8
SP - 2732
EP - 2744
JO - Biomedical Optics Express
JF - Biomedical Optics Express
SN - 2156-7085
IS - 5
ER -