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