Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping

Detalhes bibliográficos
Autor(a) principal: Piçarra, Carolina
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/56113
Resumo: Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de Ciências
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spelling Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mappinglesão cerebral traumáticatomografia computorizadalocalização de lesãosegmentação de lesãoTeses de mestrado - 2021Departamento de FísicaTese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasThe assessment of Computed Tomography (CT) scans for Traumatic Brain Injury (TBI) management remains a time consuming and challenging task for physicians. Computational methods for quantitative lesion segmentation and localisation may increase consistency in diagnosis and prognosis criteria. Our goal was to develop a registration-based tool to accurately localise several lesion classes (i.e., calculate the volume of lesion per brain region), as an extension of the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT). Lesions were located by projecting a Magnetic Resonance Imaging (MRI) labelled atlas from the Montreal Neurological Institute (MNI MRI atlas) to a lesion map in native space. We created a CT template to work as an intermediate step between the two imaging spaces, using 182 non-lesioned CT scans and an unbiased iterative registration approach. We then non-linearly registered the parcellated atlas to the CT template, subsequently registering (affine) the result to native space. From the final atlas alignment, it was possible to calculate the volume of each lesion class per brain region. This pipeline was validated on a multi-centre dataset (n=839 scans), and defined three methods to flag any scans that presented sub-optimal results. The first one was based on the similarity metric of the registration of every scan to the study-specific CT template, the second aimed to identify any scans with regions that were completely collapsed post registration, and the final one identified scans with a significant volume of intra-ventricular haemorrhage outside of the ventricles. Additionally, an assessment of lesion prevalence and of the false negative and false positive rates of the algorithm, per anatomical region, was conducted, along with a bias assessment of the BLAST-CT tool. Our results show that the constructed pipeline is able to successfully localise TBI lesions across the whole brain, although without voxel-wise accuracy. We found the error rates calculated for each brain region to be inversely correlated with the lesion volume within that region. No considerable bias was identified on BLAST-CT, as all the significant correlation coefficients calculated between the Dice scores and clinical variables (i.e., age, Glasgow Coma Scale, Extended Glasgow Outcome Scale and Injury Severity Score) were below 0.2. Our results also suggest that the variation in DSC between male and female patients within a specific age range was caused by the discrepancy in lesion volume presented by the scans included in each sample.Glocker, BenConceição, RaquelRepositório da Universidade de LisboaPiçarra, Carolina2023-02-02T09:46:51Z202120212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/56113TID:202934799enginfo: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:RCAAP2023-11-08T17:03:30Zoai:repositorio.ul.pt:10451/56113Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:06:39.867215Repositório 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 Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
title Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
spellingShingle Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
Piçarra, Carolina
lesão cerebral traumática
tomografia computorizada
localização de lesão
segmentação de lesão
Teses de mestrado - 2021
Departamento de Física
title_short Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
title_full Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
title_fullStr Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
title_full_unstemmed Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
title_sort Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
author Piçarra, Carolina
author_facet Piçarra, Carolina
author_role author
dc.contributor.none.fl_str_mv Glocker, Ben
Conceição, Raquel
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Piçarra, Carolina
dc.subject.por.fl_str_mv lesão cerebral traumática
tomografia computorizada
localização de lesão
segmentação de lesão
Teses de mestrado - 2021
Departamento de Física
topic lesão cerebral traumática
tomografia computorizada
localização de lesão
segmentação de lesão
Teses de mestrado - 2021
Departamento de Física
description Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de Ciências
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021-01-01T00:00:00Z
2023-02-02T09:46:51Z
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 http://hdl.handle.net/10451/56113
TID:202934799
url http://hdl.handle.net/10451/56113
identifier_str_mv TID:202934799
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
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