Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images

Rocío Del Amor; Sandra Morales; Adrián Colomer; José M. Mossi; David Woldbye; Kristian Klemp; Michael Larsen; Valery Naranjo

doi:10.23919/EUSIPCO.2019.8902794

Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images

Rocío Del Amor, Sandra Morales, Adrián Colomer, José M. Mossi, David Woldbye, Kristian Klemp, Michael Larsen, Valery Naranjo

Ophthalmology, Department of

4 Citations (Scopus)

Abstract

Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 µm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 µm.

Original language	English
Journal	European Signal Processing Conference
ISSN	2219-5491
DOIs	https://doi.org/10.23919/EUSIPCO.2019.8902794
Publication status	Published - 1 Sep 2019
Event	27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain Duration: 2 Sep 2019 → 6 Sep 2019

Conference

Conference	27th European Signal Processing Conference, EUSIPCO 2019
Country/Territory	Spain
City	A Coruna
Period	02/09/2019 → 06/09/2019
Sponsor	et al., National Science Foundation (NSF), Office of Naval Research Global (ONR), Turismo A Coruna, Oficina de Informacion Turismo de A Coruna, Xunta de Galicia, Centro de Investigacion TIC (CITIC), Xunta de Galicia, Conselleria de Cultura, Educacion e Ordenacion Universitaria

Keywords

Convolutional neural network
Glaucoma assessment
Layer segmentation
Optical coherence tomography
Rodent OCT

Access to Document

10.23919/EUSIPCO.2019.8902794

Cite this

@inproceedings{0d291fcd70164b4bb365d56881f9a13e,

title = "Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images",

abstract = "Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 µm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 µm.",

keywords = "Convolutional neural network, Glaucoma assessment, Layer segmentation, Optical coherence tomography, Rodent OCT",

author = "{Del Amor}, Roc{\'i}o and Sandra Morales and Adri{\'a}n Colomer and Mossi, {Jos{\'e} M.} and David Woldbye and Kristian Klemp and Michael Larsen and Valery Naranjo",

year = "2019",

month = sep,

day = "1",

doi = "10.23919/EUSIPCO.2019.8902794",

language = "English",

journal = "European Signal Processing Conference",

issn = "2219-5491",

note = "27th European Signal Processing Conference, EUSIPCO 2019 ; Conference date: 02-09-2019 Through 06-09-2019",

}

TY - GEN

T1 - Towards automatic glaucoma assessment

T2 - 27th European Signal Processing Conference, EUSIPCO 2019

AU - Del Amor, Rocío

AU - Morales, Sandra

AU - Colomer, Adrián

AU - Mossi, José M.

AU - Woldbye, David

AU - Klemp, Kristian

AU - Larsen, Michael

AU - Naranjo, Valery

PY - 2019/9/1

Y1 - 2019/9/1

N2 - Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 µm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 µm.

AB - Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 µm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 µm.

KW - Convolutional neural network

KW - Glaucoma assessment

KW - Layer segmentation

KW - Optical coherence tomography

KW - Rodent OCT

UR - http://www.scopus.com/inward/record.url?scp=85075610283&partnerID=8YFLogxK

U2 - 10.23919/EUSIPCO.2019.8902794

DO - 10.23919/EUSIPCO.2019.8902794

M3 - Conference article

AN - SCOPUS:85075610283

JO - European Signal Processing Conference

JF - European Signal Processing Conference

SN - 2219-5491

Y2 - 2 September 2019 through 6 September 2019

ER -

Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this