SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means

Autores
Scarinci, Ignacio Emanuel; Valente, Mauro Andres; Pérez, Pedro Antonio
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Nowadays, nuclear medicine procedures have become a standard for several pathologies, both for diagnosis and therapeutic purposes. Also, regarding therapeutic applications, the demand for novel techniques and new radioisotopes is increasing worldwide. Due to the high dose rates involved in therapy procedures, this aspect requires significant efforts related to the development of more accurate methods and protocols for individualized patient dosimetry estimations. New theranostic procedures allowing joint diagnosis/treatment implementation proves to be suitable for image-guided dosimetry. Therefore, appropriate image segmentation becomes a key issue for tissues/organs identification. Implementation of machine learning models for digital image processing is a promising opportunity to complement expert clinical analysis. This work presents SOCH, an original machine learning-based pipeline capable of PET/CT unsupervised automatic segmentation by heuristic algorithms means using clustering and machine learning techniques. Obtained results suggested, preliminary, that pipeline flows based on K-Means and HDBSCAN algorithms are capable of PET/CT image segmentation, proving to be a promising tool to assist expert clinicians in daily procedures.
Fil: Scarinci, Ignacio Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática. Astronomía, Física y Computación. Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); Argentina
Fil: Valente, Mauro Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática. Astronomía, Física y Computación. Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); Argentina. Universidad de La Frontera; Chile
Fil: Pérez, Pedro Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba; Facultad de Matemática, Astronomía, Física y Computación; Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); Argentina
Materia
HEURISTIC ALGORITHMS
MACHINE LEARNING
PET/CT IMAGING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/147133
Acceder
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network_name_str CONICET Digital (CONICET)
spelling SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms meansScarinci, Ignacio EmanuelValente, Mauro AndresPérez, Pedro AntonioHEURISTIC ALGORITHMSMACHINE LEARNINGPET/CT IMAGINGhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Nowadays, nuclear medicine procedures have become a standard for several pathologies, both for diagnosis and therapeutic purposes. Also, regarding therapeutic applications, the demand for novel techniques and new radioisotopes is increasing worldwide. Due to the high dose rates involved in therapy procedures, this aspect requires significant efforts related to the development of more accurate methods and protocols for individualized patient dosimetry estimations. New theranostic procedures allowing joint diagnosis/treatment implementation proves to be suitable for image-guided dosimetry. Therefore, appropriate image segmentation becomes a key issue for tissues/organs identification. Implementation of machine learning models for digital image processing is a promising opportunity to complement expert clinical analysis. This work presents SOCH, an original machine learning-based pipeline capable of PET/CT unsupervised automatic segmentation by heuristic algorithms means using clustering and machine learning techniques. Obtained results suggested, preliminary, that pipeline flows based on K-Means and HDBSCAN algorithms are capable of PET/CT image segmentation, proving to be a promising tool to assist expert clinicians in daily procedures.Fil: Scarinci, Ignacio Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática. Astronomía, Física y Computación. Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); ArgentinaFil: Valente, Mauro Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática. Astronomía, Física y Computación. Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); Argentina. Universidad de La Frontera; ChileFil: Pérez, Pedro Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba; Facultad de Matemática, Astronomía, Física y Computación; Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); ArgentinaElsevier Ltd2020-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/147133Scarinci, Ignacio Emanuel; Valente, Mauro Andres; Pérez, Pedro Antonio; SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means; Elsevier Ltd; Informatics in Medicine Unlocked; 21; 1-2020; 1-92352-9148CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2352914820306328info:eu-repo/semantics/altIdentifier/doi/10.1016/j.imu.2020.100481info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:14:16Zoai:ri.conicet.gov.ar:11336/147133instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:14:16.882CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
title SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
spellingShingle SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
Scarinci, Ignacio Emanuel
HEURISTIC ALGORITHMS
MACHINE LEARNING
PET/CT IMAGING
title_short SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
title_full SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
title_fullStr SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
title_full_unstemmed SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
title_sort SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means
dc.creator.none.fl_str_mv Scarinci, Ignacio Emanuel
Valente, Mauro Andres
Pérez, Pedro Antonio
author Scarinci, Ignacio Emanuel
author_facet Scarinci, Ignacio Emanuel
Valente, Mauro Andres
Pérez, Pedro Antonio
author_role author
author2 Valente, Mauro Andres
Pérez, Pedro Antonio
author2_role author
author
dc.subject.none.fl_str_mv HEURISTIC ALGORITHMS
MACHINE LEARNING
PET/CT IMAGING
topic HEURISTIC ALGORITHMS
MACHINE LEARNING
PET/CT IMAGING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Nowadays, nuclear medicine procedures have become a standard for several pathologies, both for diagnosis and therapeutic purposes. Also, regarding therapeutic applications, the demand for novel techniques and new radioisotopes is increasing worldwide. Due to the high dose rates involved in therapy procedures, this aspect requires significant efforts related to the development of more accurate methods and protocols for individualized patient dosimetry estimations. New theranostic procedures allowing joint diagnosis/treatment implementation proves to be suitable for image-guided dosimetry. Therefore, appropriate image segmentation becomes a key issue for tissues/organs identification. Implementation of machine learning models for digital image processing is a promising opportunity to complement expert clinical analysis. This work presents SOCH, an original machine learning-based pipeline capable of PET/CT unsupervised automatic segmentation by heuristic algorithms means using clustering and machine learning techniques. Obtained results suggested, preliminary, that pipeline flows based on K-Means and HDBSCAN algorithms are capable of PET/CT image segmentation, proving to be a promising tool to assist expert clinicians in daily procedures.
Fil: Scarinci, Ignacio Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática. Astronomía, Física y Computación. Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); Argentina
Fil: Valente, Mauro Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática. Astronomía, Física y Computación. Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); Argentina. Universidad de La Frontera; Chile
Fil: Pérez, Pedro Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba; Facultad de Matemática, Astronomía, Física y Computación; Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx); Argentina
description Nowadays, nuclear medicine procedures have become a standard for several pathologies, both for diagnosis and therapeutic purposes. Also, regarding therapeutic applications, the demand for novel techniques and new radioisotopes is increasing worldwide. Due to the high dose rates involved in therapy procedures, this aspect requires significant efforts related to the development of more accurate methods and protocols for individualized patient dosimetry estimations. New theranostic procedures allowing joint diagnosis/treatment implementation proves to be suitable for image-guided dosimetry. Therefore, appropriate image segmentation becomes a key issue for tissues/organs identification. Implementation of machine learning models for digital image processing is a promising opportunity to complement expert clinical analysis. This work presents SOCH, an original machine learning-based pipeline capable of PET/CT unsupervised automatic segmentation by heuristic algorithms means using clustering and machine learning techniques. Obtained results suggested, preliminary, that pipeline flows based on K-Means and HDBSCAN algorithms are capable of PET/CT image segmentation, proving to be a promising tool to assist expert clinicians in daily procedures.
publishDate 2020
dc.date.none.fl_str_mv 2020-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/147133
Scarinci, Ignacio Emanuel; Valente, Mauro Andres; Pérez, Pedro Antonio; SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means; Elsevier Ltd; Informatics in Medicine Unlocked; 21; 1-2020; 1-9
2352-9148
CONICET Digital
CONICET
url http://hdl.handle.net/11336/147133
identifier_str_mv Scarinci, Ignacio Emanuel; Valente, Mauro Andres; Pérez, Pedro Antonio; SOCH. An ML-based pipeline for PET automatic segmentation by heuristic algorithms means; Elsevier Ltd; Informatics in Medicine Unlocked; 21; 1-2020; 1-9
2352-9148
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2352914820306328
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.imu.2020.100481
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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