From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy
- Autores
- Viglietti, Julia S.; Espain, Maria Sol; Díaz, Rodrigo Fernando; Nieto, Luis Agustin; Szewc, Manuel; Bernardi, Guillermo Carlos; Rodríguez, Luis M.; Fregenal, Daniel Eduardo; Saint Martin, María Laura Gisela; Portu, Agustina Mariana
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Knowledge of the 10B microdistribution is of great relevance in BNCT studies. Since 10B concentration assesment through neutron autoradiography depends on the correct quantification of tracks in a nuclear track detector, image acquisition and processing conditions should be controlled and verified, in order to obtain accurate results to be applied in the frame of BNCT. With this aim, an image verification process was proposed, based on parameters extracted from the quantified nuclear tracks. Track characterization was performed by selecting a set of morphological and pixel-intensity uniformity parameters from the quantified objects (area, diameter, roundness, aspect ratio, heterogeneity and clumpiness). Their distributions were studied, leading to the observation of varying behaviours in images generated by different samples and acquisition conditions. The distributions corresponding to samples coming from the BNC reaction showed similar attributes in each analyzed parameter, proving to be robust to the experimental process, but sensitive to light and focus conditions. Considering those observations, a manual feature extraction was performed as a pre-processing step. A Support Vector Machine (SVM) and a fully dense Neural Network (NN) were optimized, trained, and tested. The final performance metrics were similar for both models: 93%-93% for the SVM, vs 94%-95% for the NN in accuracy and precision respectively. Based on the distribution of the predicted class probabilities, the latter had a better capacity to reject inadequate images, so the NN was selected to perform the image verification step prior to quantification. The trained NN was able to correctly classify the images regardless of their track density. The exhaustive characterization of the nuclear tracks provided new knowledge related to the autoradiographic images generation. The inclusion of machine learning in the analysis workflow proves to optimize the boron determination process and paves the way for further applications in the field of boron imaging.
Fil: Viglietti, Julia S.. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina
Fil: Espain, Maria Sol. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina
Fil: Díaz, Rodrigo Fernando. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina
Fil: Nieto, Luis Agustin. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina
Fil: Szewc, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cincinnati; Estados Unidos
Fil: Bernardi, Guillermo Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
Fil: Rodríguez, Luis M.. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fregenal, Daniel Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
Fil: Saint Martin, María Laura Gisela. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina
Fil: Portu, Agustina Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina - Materia
-
AUTORADIOGRAPHY
NEUTRON
MACHINE LEARNING
NUCLEAR TRACKS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/222662
Ver los metadatos del registro completo
id |
CONICETDig_67f815e1cd580c752f43fc8f3cf4d574 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/222662 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
spelling |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapyViglietti, Julia S.Espain, Maria SolDíaz, Rodrigo FernandoNieto, Luis AgustinSzewc, ManuelBernardi, Guillermo CarlosRodríguez, Luis M.Fregenal, Daniel EduardoSaint Martin, María Laura GiselaPortu, Agustina MarianaAUTORADIOGRAPHYNEUTRONMACHINE LEARNINGNUCLEAR TRACKShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Knowledge of the 10B microdistribution is of great relevance in BNCT studies. Since 10B concentration assesment through neutron autoradiography depends on the correct quantification of tracks in a nuclear track detector, image acquisition and processing conditions should be controlled and verified, in order to obtain accurate results to be applied in the frame of BNCT. With this aim, an image verification process was proposed, based on parameters extracted from the quantified nuclear tracks. Track characterization was performed by selecting a set of morphological and pixel-intensity uniformity parameters from the quantified objects (area, diameter, roundness, aspect ratio, heterogeneity and clumpiness). Their distributions were studied, leading to the observation of varying behaviours in images generated by different samples and acquisition conditions. The distributions corresponding to samples coming from the BNC reaction showed similar attributes in each analyzed parameter, proving to be robust to the experimental process, but sensitive to light and focus conditions. Considering those observations, a manual feature extraction was performed as a pre-processing step. A Support Vector Machine (SVM) and a fully dense Neural Network (NN) were optimized, trained, and tested. The final performance metrics were similar for both models: 93%-93% for the SVM, vs 94%-95% for the NN in accuracy and precision respectively. Based on the distribution of the predicted class probabilities, the latter had a better capacity to reject inadequate images, so the NN was selected to perform the image verification step prior to quantification. The trained NN was able to correctly classify the images regardless of their track density. The exhaustive characterization of the nuclear tracks provided new knowledge related to the autoradiographic images generation. The inclusion of machine learning in the analysis workflow proves to optimize the boron determination process and paves the way for further applications in the field of boron imaging.Fil: Viglietti, Julia S.. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; ArgentinaFil: Espain, Maria Sol. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; ArgentinaFil: Díaz, Rodrigo Fernando. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; ArgentinaFil: Nieto, Luis Agustin. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; ArgentinaFil: Szewc, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cincinnati; Estados UnidosFil: Bernardi, Guillermo Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; ArgentinaFil: Rodríguez, Luis M.. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fregenal, Daniel Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; ArgentinaFil: Saint Martin, María Laura Gisela. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; ArgentinaFil: Portu, Agustina Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; ArgentinaPublic Library of Science2023-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/222662Viglietti, Julia S.; Espain, Maria Sol; Díaz, Rodrigo Fernando; Nieto, Luis Agustin; Szewc, Manuel; et al.; From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy; Public Library of Science; Plos One; 18; 12; e0293891; 12-2023; 1-231932-6203CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293891info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0293891info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:20:53Zoai:ri.conicet.gov.ar:11336/222662instacron: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:34982025-09-29 10:20:53.747CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy |
title |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy |
spellingShingle |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy Viglietti, Julia S. AUTORADIOGRAPHY NEUTRON MACHINE LEARNING NUCLEAR TRACKS |
title_short |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy |
title_full |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy |
title_fullStr |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy |
title_full_unstemmed |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy |
title_sort |
From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy |
dc.creator.none.fl_str_mv |
Viglietti, Julia S. Espain, Maria Sol Díaz, Rodrigo Fernando Nieto, Luis Agustin Szewc, Manuel Bernardi, Guillermo Carlos Rodríguez, Luis M. Fregenal, Daniel Eduardo Saint Martin, María Laura Gisela Portu, Agustina Mariana |
author |
Viglietti, Julia S. |
author_facet |
Viglietti, Julia S. Espain, Maria Sol Díaz, Rodrigo Fernando Nieto, Luis Agustin Szewc, Manuel Bernardi, Guillermo Carlos Rodríguez, Luis M. Fregenal, Daniel Eduardo Saint Martin, María Laura Gisela Portu, Agustina Mariana |
author_role |
author |
author2 |
Espain, Maria Sol Díaz, Rodrigo Fernando Nieto, Luis Agustin Szewc, Manuel Bernardi, Guillermo Carlos Rodríguez, Luis M. Fregenal, Daniel Eduardo Saint Martin, María Laura Gisela Portu, Agustina Mariana |
author2_role |
author author author author author author author author author |
dc.subject.none.fl_str_mv |
AUTORADIOGRAPHY NEUTRON MACHINE LEARNING NUCLEAR TRACKS |
topic |
AUTORADIOGRAPHY NEUTRON MACHINE LEARNING NUCLEAR TRACKS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Knowledge of the 10B microdistribution is of great relevance in BNCT studies. Since 10B concentration assesment through neutron autoradiography depends on the correct quantification of tracks in a nuclear track detector, image acquisition and processing conditions should be controlled and verified, in order to obtain accurate results to be applied in the frame of BNCT. With this aim, an image verification process was proposed, based on parameters extracted from the quantified nuclear tracks. Track characterization was performed by selecting a set of morphological and pixel-intensity uniformity parameters from the quantified objects (area, diameter, roundness, aspect ratio, heterogeneity and clumpiness). Their distributions were studied, leading to the observation of varying behaviours in images generated by different samples and acquisition conditions. The distributions corresponding to samples coming from the BNC reaction showed similar attributes in each analyzed parameter, proving to be robust to the experimental process, but sensitive to light and focus conditions. Considering those observations, a manual feature extraction was performed as a pre-processing step. A Support Vector Machine (SVM) and a fully dense Neural Network (NN) were optimized, trained, and tested. The final performance metrics were similar for both models: 93%-93% for the SVM, vs 94%-95% for the NN in accuracy and precision respectively. Based on the distribution of the predicted class probabilities, the latter had a better capacity to reject inadequate images, so the NN was selected to perform the image verification step prior to quantification. The trained NN was able to correctly classify the images regardless of their track density. The exhaustive characterization of the nuclear tracks provided new knowledge related to the autoradiographic images generation. The inclusion of machine learning in the analysis workflow proves to optimize the boron determination process and paves the way for further applications in the field of boron imaging. Fil: Viglietti, Julia S.. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina Fil: Espain, Maria Sol. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina Fil: Díaz, Rodrigo Fernando. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina Fil: Nieto, Luis Agustin. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina Fil: Szewc, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cincinnati; Estados Unidos Fil: Bernardi, Guillermo Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina Fil: Rodríguez, Luis M.. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Fregenal, Daniel Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina Fil: Saint Martin, María Laura Gisela. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina Fil: Portu, Agustina Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Comisión Nacional de Energía Atómica. Gerencia de Área de Aplicaciones de la Tecnología Nuclear. Departamento de Radiobiología; Argentina |
description |
Knowledge of the 10B microdistribution is of great relevance in BNCT studies. Since 10B concentration assesment through neutron autoradiography depends on the correct quantification of tracks in a nuclear track detector, image acquisition and processing conditions should be controlled and verified, in order to obtain accurate results to be applied in the frame of BNCT. With this aim, an image verification process was proposed, based on parameters extracted from the quantified nuclear tracks. Track characterization was performed by selecting a set of morphological and pixel-intensity uniformity parameters from the quantified objects (area, diameter, roundness, aspect ratio, heterogeneity and clumpiness). Their distributions were studied, leading to the observation of varying behaviours in images generated by different samples and acquisition conditions. The distributions corresponding to samples coming from the BNC reaction showed similar attributes in each analyzed parameter, proving to be robust to the experimental process, but sensitive to light and focus conditions. Considering those observations, a manual feature extraction was performed as a pre-processing step. A Support Vector Machine (SVM) and a fully dense Neural Network (NN) were optimized, trained, and tested. The final performance metrics were similar for both models: 93%-93% for the SVM, vs 94%-95% for the NN in accuracy and precision respectively. Based on the distribution of the predicted class probabilities, the latter had a better capacity to reject inadequate images, so the NN was selected to perform the image verification step prior to quantification. The trained NN was able to correctly classify the images regardless of their track density. The exhaustive characterization of the nuclear tracks provided new knowledge related to the autoradiographic images generation. The inclusion of machine learning in the analysis workflow proves to optimize the boron determination process and paves the way for further applications in the field of boron imaging. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12 |
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/11336/222662 Viglietti, Julia S.; Espain, Maria Sol; Díaz, Rodrigo Fernando; Nieto, Luis Agustin; Szewc, Manuel; et al.; From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy; Public Library of Science; Plos One; 18; 12; e0293891; 12-2023; 1-23 1932-6203 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/222662 |
identifier_str_mv |
Viglietti, Julia S.; Espain, Maria Sol; Díaz, Rodrigo Fernando; Nieto, Luis Agustin; Szewc, Manuel; et al.; From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy; Public Library of Science; Plos One; 18; 12; e0293891; 12-2023; 1-23 1932-6203 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://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293891 info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0293891 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Public Library of Science |
publisher.none.fl_str_mv |
Public Library of Science |
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 |
_version_ |
1844614193580343296 |
score |
13.070432 |