Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography

The study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficultie...

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Autores: Herrera, D, Ochoa-Ruiz, G, Stephan-Otto, C, Gonzalez-Mendoza, M, Munuera, J, Mata, C
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)
Repositorio:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau
OAI Identifier:oai:iibsantpau.fundanetsuite.com:p20648
Acceso en línea:https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20648
Access Level:acceso abierto
Palabra clave:Deep learning
Denoising
Segmentation
Vessels
MRI
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spelling Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiographyHerrera, DOchoa-Ruiz, GStephan-Otto, CGonzalez-Mendoza, MMunuera, JMata, CDeep learningDenoisingSegmentationVesselsMRIThe study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficulties in the quantification of the vessel patterns. Depending on the patient, specialists can use manual procedures to analyze the images and segment the veins during the image analysis. Image acquisition in infants is often affected by motion artifacts, variable contrast due to short acquisition times, and scanner hardware limitations, which together increase noise and reduce vessel visibility. Furthermore, this fact poses serious challenges for both the use of AI tools as well as the analysis and diagnosis of professionals. The goal of this research is to assess automatic denoising pipelines for enhancing image quality to aid visual analysis and automated vessel segmentation for improved quantification through vessel feature extraction. As a result of this research, an entire pipeline is presented as a solution. For denoising the images, we have explored the combination of conventional techniques with unsupervised techniques based on deep learning. The outcomes were subjectively assessed by experts and quantitatively by non-reference image quality evaluators. Using Noise2Void and PPN2V GMM produced the best outcomes, according to the scores. However, employing a combination of traditional methods and deep learning-based methods, the vessels showed a reduction in noise in the central and most dense areas, according to qualitative results. A model was trained using noisy images for segmentation. Then it was put to the test using both denoised and noisy images. The findings demonstrated an improvement of 9.4% in the dice score and nearly 16% in the Hausdorff distance when the model was trained using noisy images and segmentation was obtained using denoised images.SPRINGER2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20648Health Information Science and SystemsISSN: 20472501reponame:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pauinstname:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)Inglésinfo:eu-repo/semantics/openAccessoai:iibsantpau.fundanetsuite.com:p206482026-06-14T12:41:47Z
dc.title.none.fl_str_mv Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
spellingShingle Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
Herrera, D
Deep learning
Denoising
Segmentation
Vessels
MRI
title_short Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_full Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_fullStr Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_full_unstemmed Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_sort Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
dc.creator.none.fl_str_mv Herrera, D
Ochoa-Ruiz, G
Stephan-Otto, C
Gonzalez-Mendoza, M
Munuera, J
Mata, C
author Herrera, D
author_facet Herrera, D
Ochoa-Ruiz, G
Stephan-Otto, C
Gonzalez-Mendoza, M
Munuera, J
Mata, C
author_role author
author2 Ochoa-Ruiz, G
Stephan-Otto, C
Gonzalez-Mendoza, M
Munuera, J
Mata, C
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Deep learning
Denoising
Segmentation
Vessels
MRI
topic Deep learning
Denoising
Segmentation
Vessels
MRI
description The study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficulties in the quantification of the vessel patterns. Depending on the patient, specialists can use manual procedures to analyze the images and segment the veins during the image analysis. Image acquisition in infants is often affected by motion artifacts, variable contrast due to short acquisition times, and scanner hardware limitations, which together increase noise and reduce vessel visibility. Furthermore, this fact poses serious challenges for both the use of AI tools as well as the analysis and diagnosis of professionals. The goal of this research is to assess automatic denoising pipelines for enhancing image quality to aid visual analysis and automated vessel segmentation for improved quantification through vessel feature extraction. As a result of this research, an entire pipeline is presented as a solution. For denoising the images, we have explored the combination of conventional techniques with unsupervised techniques based on deep learning. The outcomes were subjectively assessed by experts and quantitatively by non-reference image quality evaluators. Using Noise2Void and PPN2V GMM produced the best outcomes, according to the scores. However, employing a combination of traditional methods and deep learning-based methods, the vessels showed a reduction in noise in the central and most dense areas, according to qualitative results. A model was trained using noisy images for segmentation. Then it was put to the test using both denoised and noisy images. The findings demonstrated an improvement of 9.4% in the dice score and nearly 16% in the Hausdorff distance when the model was trained using noisy images and segmentation was obtained using denoised images.
publishDate 2025
dc.date.none.fl_str_mv 2025
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url https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20648
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv SPRINGER
publisher.none.fl_str_mv SPRINGER
dc.source.none.fl_str_mv Health Information Science and Systems
ISSN: 20472501
reponame:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau
instname:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)
instname_str Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)
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