Abstract
The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.
| Original language | English (US) |
|---|---|
| Article number | #326568 |
| Pages (from-to) | 3092-3105 |
| Number of pages | 14 |
| Journal | Biomedical Optics Express |
| Volume | 9 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 2018 |
Fingerprint
ASJC Scopus subject areas
- Biotechnology
- Atomic and Molecular Physics, and Optics
Cite this
Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases. / Camino, Acner; Wang, Zhuo; Wang, Jie; Pennesi, Mark; Yang, Paul; Huang, David; Li, Dengwang; Yali, Jia.
In: Biomedical Optics Express, Vol. 9, No. 7, #326568, 01.07.2018, p. 3092-3105.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases
AU - Camino, Acner
AU - Wang, Zhuo
AU - Wang, Jie
AU - Pennesi, Mark
AU - Yang, Paul
AU - Huang, David
AU - Li, Dengwang
AU - Yali, Jia
PY - 2018/7/1
Y1 - 2018/7/1
N2 - The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.
AB - The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.
UR - http://www.scopus.com/inward/record.url?scp=85049377535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049377535&partnerID=8YFLogxK
U2 - 10.1364/BOE.9.003092
DO - 10.1364/BOE.9.003092
M3 - Article
AN - SCOPUS:85049377535
VL - 9
SP - 3092
EP - 3105
JO - Biomedical Optics Express
JF - Biomedical Optics Express
SN - 2156-7085
IS - 7
M1 - #326568
ER -