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

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Towards automatic glaucoma assessment : An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images. / Del Amor, Rocío; Morales, Sandra; Colomer, Adrián; Mossi, José M.; Woldbye, David; Klemp, Kristian; Larsen, Michael; Naranjo, Valery.

EUSIPCO 2019 - 27th European Signal Processing Conference. European Signal Processing Conference, EUSIPCO, 2019. (European Signal Processing Conference, Vol. 2019-September).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Del Amor, R, Morales, S, Colomer, A, Mossi, JM, Woldbye, D, Klemp, K, Larsen, M & Naranjo, V 2019, Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images. in EUSIPCO 2019 - 27th European Signal Processing Conference. European Signal Processing Conference, EUSIPCO, European Signal Processing Conference, vol. 2019-September, 27th European Signal Processing Conference, EUSIPCO 2019, A Coruna, Spain, 02/09/2019. https://doi.org/10.23919/EUSIPCO.2019.8902794

APA

Del Amor, R., Morales, S., Colomer, A., Mossi, J. M., Woldbye, D., Klemp, K., Larsen, M., & Naranjo, V. (2019). Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images. In EUSIPCO 2019 - 27th European Signal Processing Conference European Signal Processing Conference, EUSIPCO. European Signal Processing Conference Vol. 2019-September https://doi.org/10.23919/EUSIPCO.2019.8902794

Vancouver

Del Amor R, Morales S, Colomer A, Mossi JM, Woldbye D, Klemp K et al. Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images. In EUSIPCO 2019 - 27th European Signal Processing Conference. European Signal Processing Conference, EUSIPCO. 2019. (European Signal Processing Conference, Vol. 2019-September). https://doi.org/10.23919/EUSIPCO.2019.8902794

Author

Del Amor, Rocío ; Morales, Sandra ; Colomer, Adrián ; Mossi, José M. ; Woldbye, David ; Klemp, Kristian ; Larsen, Michael ; Naranjo, Valery. / Towards automatic glaucoma assessment : An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images. EUSIPCO 2019 - 27th European Signal Processing Conference. European Signal Processing Conference, EUSIPCO, 2019. (European Signal Processing Conference, Vol. 2019-September).

Bibtex

@inproceedings{3556461853ce483baa66219805318078,
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,
doi = "10.23919/EUSIPCO.2019.8902794",
language = "English",
series = "European Signal Processing Conference",
booktitle = "EUSIPCO 2019 - 27th European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
note = "27th European Signal Processing Conference, EUSIPCO 2019 ; Conference date: 02-09-2019 Through 06-09-2019",

}

RIS

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

Y1 - 2019/9

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

U2 - 10.23919/EUSIPCO.2019.8902794

DO - 10.23919/EUSIPCO.2019.8902794

M3 - Article in proceedings

AN - SCOPUS:85075610283

T3 - European Signal Processing Conference

BT - EUSIPCO 2019 - 27th European Signal Processing Conference

PB - European Signal Processing Conference, EUSIPCO

Y2 - 2 September 2019 through 6 September 2019

ER -

ID: 241088792