Visual Odometer on Videos of Endoscopic Capsules (VOVEC)
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/113958 |
Resumo: | Since its introduction in 2001, capsule endoscopy has become the leading screening method for the small bowel - a region not easily accessible with traditional endoscopy techniques - revolutionizing the way diagnostics work in the field of small bowel diseases. These capsules are vitamin-sized and leverage from a small wireless camera to create 8 to 10 hour videos of the patients digestive tract. Due to the long duration of the videos produced, the human-based diagnosis is elongated, tedious and error-prone. Moreover, once a lesion is found, the localization information is scarce and hardware dependent, entailing desirability for a software-only endoscopic capsule localization system with added precision. This work stems from this need and, bearing this in mind, we propose the implementation of two deep-learning based methods to improve upon the limitations of the techniques used so far for the capsule position estimation. To train and test our networks, a dataset of 111 PillCam SB3 and 338 PillCam SB2 videos were used, courtesy of Centro Hospitalar do Porto (CHP). The first method consists in a simple capsule displacement estimation throughout the small bowel utilizing HomographyNet, a deep learning supervised approach that is used for homography computation between images. (DeTone et al. (2016)) Differently, the second proposed method is intended to provide a 3D position along the small intestine, utilizing a deep learning unsupervised approach labeled SfMLearner, which takes advantage of a combination between a DepthNet and a PoseNet to learn depth and ego-motion from video simultaneously. (Zhou et al. (2017)) |
id |
RCAP_ca577e6d75be50807cc211aa67fb0225 |
---|---|
oai_identifier_str |
oai:repositorio-aberto.up.pt:10216/113958 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC)Engenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringSince its introduction in 2001, capsule endoscopy has become the leading screening method for the small bowel - a region not easily accessible with traditional endoscopy techniques - revolutionizing the way diagnostics work in the field of small bowel diseases. These capsules are vitamin-sized and leverage from a small wireless camera to create 8 to 10 hour videos of the patients digestive tract. Due to the long duration of the videos produced, the human-based diagnosis is elongated, tedious and error-prone. Moreover, once a lesion is found, the localization information is scarce and hardware dependent, entailing desirability for a software-only endoscopic capsule localization system with added precision. This work stems from this need and, bearing this in mind, we propose the implementation of two deep-learning based methods to improve upon the limitations of the techniques used so far for the capsule position estimation. To train and test our networks, a dataset of 111 PillCam SB3 and 338 PillCam SB2 videos were used, courtesy of Centro Hospitalar do Porto (CHP). The first method consists in a simple capsule displacement estimation throughout the small bowel utilizing HomographyNet, a deep learning supervised approach that is used for homography computation between images. (DeTone et al. (2016)) Differently, the second proposed method is intended to provide a 3D position along the small intestine, utilizing a deep learning unsupervised approach labeled SfMLearner, which takes advantage of a combination between a DepthNet and a PoseNet to learn depth and ego-motion from video simultaneously. (Zhou et al. (2017))2018-07-132018-07-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/113958TID:202114732engGil Martins Pinheiroinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T13:29:54Zoai:repositorio-aberto.up.pt:10216/113958Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:41:38.351472Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) |
title |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) |
spellingShingle |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) Gil Martins Pinheiro Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) |
title_full |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) |
title_fullStr |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) |
title_full_unstemmed |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) |
title_sort |
Visual Odometer on Videos of Endoscopic Capsules (VOVEC) |
author |
Gil Martins Pinheiro |
author_facet |
Gil Martins Pinheiro |
author_role |
author |
dc.contributor.author.fl_str_mv |
Gil Martins Pinheiro |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
description |
Since its introduction in 2001, capsule endoscopy has become the leading screening method for the small bowel - a region not easily accessible with traditional endoscopy techniques - revolutionizing the way diagnostics work in the field of small bowel diseases. These capsules are vitamin-sized and leverage from a small wireless camera to create 8 to 10 hour videos of the patients digestive tract. Due to the long duration of the videos produced, the human-based diagnosis is elongated, tedious and error-prone. Moreover, once a lesion is found, the localization information is scarce and hardware dependent, entailing desirability for a software-only endoscopic capsule localization system with added precision. This work stems from this need and, bearing this in mind, we propose the implementation of two deep-learning based methods to improve upon the limitations of the techniques used so far for the capsule position estimation. To train and test our networks, a dataset of 111 PillCam SB3 and 338 PillCam SB2 videos were used, courtesy of Centro Hospitalar do Porto (CHP). The first method consists in a simple capsule displacement estimation throughout the small bowel utilizing HomographyNet, a deep learning supervised approach that is used for homography computation between images. (DeTone et al. (2016)) Differently, the second proposed method is intended to provide a 3D position along the small intestine, utilizing a deep learning unsupervised approach labeled SfMLearner, which takes advantage of a combination between a DepthNet and a PoseNet to learn depth and ego-motion from video simultaneously. (Zhou et al. (2017)) |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-07-13 2018-07-13T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/113958 TID:202114732 |
url |
https://hdl.handle.net/10216/113958 |
identifier_str_mv |
TID:202114732 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799135731072892928 |