dc.contributor.author |
del Amor, Rocío
|
es_ES |
dc.contributor.author |
Morales, Sandra
|
es_ES |
dc.contributor.author |
Colomer, Adrián
|
es_ES |
dc.contributor.author |
Mossi García, José Manuel
|
es_ES |
dc.contributor.author |
Woldbye, David
|
es_ES |
dc.contributor.author |
Klemp, Kristian
|
es_ES |
dc.contributor.author |
Larsen, Michael
|
es_ES |
dc.contributor.author |
Naranjo Ornedo, Valeriana
|
es_ES |
dc.date.accessioned |
2019-09-17T12:32:43Z |
|
dc.date.available |
2019-09-17T12:32:43Z |
|
dc.date.issued |
2019 |
|
dc.identifier.isbn |
978-9-0827-9702-2 |
|
dc.identifier.issn |
2076-1465 |
|
dc.identifier.uri |
http://hdl.handle.net/10251/125893 |
|
dc.description.abstract |
[EN] 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. |
es_ES |
dc.description.sponsorship |
This work was supported by the Project GALAHAD [H2020-ICT-2016-2017, 732613]. |
es_ES |
dc.format.extent |
5 |
es_ES |
dc.language |
Inglés |
es_ES |
dc.publisher |
IEEE |
es_ES |
dc.relation.ispartof |
2019 27th European Signal Processing Conference (EUSIPCO) |
es_ES |
dc.rights |
Reserva de todos los derechos |
es_ES |
dc.subject |
Optical coherence tomography |
es_ES |
dc.subject |
Rodent OCT |
es_ES |
dc.subject |
Layer segmentation |
es_ES |
dc.subject |
Convolutional neural networks |
es_ES |
dc.subject |
Glaucoma assessment |
es_ES |
dc.subject.classification |
TEORIA DE LA SEÑAL Y COMUNICACIONES |
es_ES |
dc.title |
Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images |
es_ES |
dc.type |
Capítulo de libro |
es_ES |
dc.type |
Comunicación en congreso |
es_ES |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/H2020/732613/EU |
es_ES |
dc.rights.accessRights |
Abierto |
es_ES |
dc.contributor.affiliation |
Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia |
es_ES |
dc.description.bibliographicCitation |
Del Amor, R.; Morales, S.; Colomer, A.; Mossi García, JM.; Woldbye, D.; Klemp, K.; Larsen, M.... (2019). Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images. En 2019 27th European Signal Processing Conference (EUSIPCO). IEEE. http://hdl.handle.net/10251/125893 |
es_ES |
dc.description.accrualMethod |
S |
es_ES |
dc.relation.conferencename |
European Signal Processing Conference (EUSIPCO) (formerly European Signal and Image Processing Conference) |
es_ES |
dc.relation.conferencedate |
Septiembre 02-07,2019 |
es_ES |
dc.relation.conferenceplace |
A Coruña, España |
es_ES |
dc.type.version |
info:eu-repo/semantics/publishedVersion |
es_ES |
dc.relation.pasarela |
S\393233 |
es_ES |
dc.contributor.funder |
European Commission |
es_ES |