Advancing laser ablation assessment in hyperspectral imaging through machine learning

Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need...

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Autores: Danilov, Viacheslav V., De Landro, Martina, Felli, Eric, Barberio, Manuel, Diana, Michele, Saccomandi, Paola
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/71740
Acceso en línea:http://hdl.handle.net/10230/71740
http://dx.doi.org/10.1016/j.compbiomed.2024.108849
Access Level:acceso abierto
Palabra clave:Hyperspectral imaging
Tissue ablation
Object detection
Clustering
Segmentation
Dimensionality reduction
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spelling Advancing laser ablation assessment in hyperspectral imaging through machine learningDanilov, Viacheslav V.De Landro, MartinaFelli, EricBarberio, ManuelDiana, MicheleSaccomandi, PaolaHyperspectral imagingTissue ablationObject detectionClusteringSegmentationDimensionality reductionHyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need for end-to-end image processing workflows specifically tailored to handle these data. This study addresses this challenge by proposing a multi-stage workflow for the analysis of hyperspectral data and allows investigating the performance of different components and modalities for ablation detection and segmentation. To address dimensionality reduction, we integrated principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to capture dominant variations and reveal intricate structures, respectively. Additionally, we employed the Faster Region-based Convolutional Neural Network (Faster R–CNN) to accurately localize ablation areas. The two-stage detection process of Faster R–CNN, along with the choice of dimensionality reduction technique and data modality, significantly influenced the performance in detecting ablation areas. The evaluation of the ablation detection on an independent test set demonstrated a mean average precision of approximately 0.74, which validates the generalization ability of the models. In the segmentation component, the Mean Shift algorithm showed high quality segmentation without manual cluster definition. Our results prove that the integration of PCA, t-SNE, and Faster R–CNN enables improved interpretation of hyperspectral data, leading to the development of reliable ablation detection and segmentation systems.This study has been supported by the research project "HyperSIGHT" (No. R18SF4YHHS, "Hyperspectral imagery algorithms for processing of multimodal data: application in thermal monitoring") funded by the Italian Ministry of Education, University, and Research. The project has also received funding from the European Research Council under the Horizon 2020 Research and Innovation Program of the European Union (Grant agreement No. 759159, "Laser ablation: selectivity and monitoring for optimal tumor removal", https://doi.org/10.3030/759159). Additionally, this study was partially funded by a grant from the ARC Foundation for Cancer Research as part of the ELIOS (Endoscopic Luminescent Imaging for Precision Oncologic Surgery) project.Elsevier202520252024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/71740http://dx.doi.org/10.1016/j.compbiomed.2024.108849http://hdl.handle.net/10230/71740reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésComputers in Biology and Medicine. 2024 Sep;179:108849info:eu-repo/grantAgreement/EC/H2020/759159© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/717402026-05-29T05:05:01Z
dc.title.none.fl_str_mv Advancing laser ablation assessment in hyperspectral imaging through machine learning
title Advancing laser ablation assessment in hyperspectral imaging through machine learning
spellingShingle Advancing laser ablation assessment in hyperspectral imaging through machine learning
Danilov, Viacheslav V.
Hyperspectral imaging
Tissue ablation
Object detection
Clustering
Segmentation
Dimensionality reduction
title_short Advancing laser ablation assessment in hyperspectral imaging through machine learning
title_full Advancing laser ablation assessment in hyperspectral imaging through machine learning
title_fullStr Advancing laser ablation assessment in hyperspectral imaging through machine learning
title_full_unstemmed Advancing laser ablation assessment in hyperspectral imaging through machine learning
title_sort Advancing laser ablation assessment in hyperspectral imaging through machine learning
dc.creator.none.fl_str_mv Danilov, Viacheslav V.
De Landro, Martina
Felli, Eric
Barberio, Manuel
Diana, Michele
Saccomandi, Paola
author Danilov, Viacheslav V.
author_facet Danilov, Viacheslav V.
De Landro, Martina
Felli, Eric
Barberio, Manuel
Diana, Michele
Saccomandi, Paola
author_role author
author2 De Landro, Martina
Felli, Eric
Barberio, Manuel
Diana, Michele
Saccomandi, Paola
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Hyperspectral imaging
Tissue ablation
Object detection
Clustering
Segmentation
Dimensionality reduction
topic Hyperspectral imaging
Tissue ablation
Object detection
Clustering
Segmentation
Dimensionality reduction
description Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need for end-to-end image processing workflows specifically tailored to handle these data. This study addresses this challenge by proposing a multi-stage workflow for the analysis of hyperspectral data and allows investigating the performance of different components and modalities for ablation detection and segmentation. To address dimensionality reduction, we integrated principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to capture dominant variations and reveal intricate structures, respectively. Additionally, we employed the Faster Region-based Convolutional Neural Network (Faster R–CNN) to accurately localize ablation areas. The two-stage detection process of Faster R–CNN, along with the choice of dimensionality reduction technique and data modality, significantly influenced the performance in detecting ablation areas. The evaluation of the ablation detection on an independent test set demonstrated a mean average precision of approximately 0.74, which validates the generalization ability of the models. In the segmentation component, the Mean Shift algorithm showed high quality segmentation without manual cluster definition. Our results prove that the integration of PCA, t-SNE, and Faster R–CNN enables improved interpretation of hyperspectral data, leading to the development of reliable ablation detection and segmentation systems.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/71740
http://dx.doi.org/10.1016/j.compbiomed.2024.108849
http://hdl.handle.net/10230/71740
url http://hdl.handle.net/10230/71740
http://dx.doi.org/10.1016/j.compbiomed.2024.108849
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computers in Biology and Medicine. 2024 Sep;179:108849
info:eu-repo/grantAgreement/EC/H2020/759159
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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