Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models

Objective: Segmentation of brain sulci in pre-term infants is crucial for monitoring their development. While magnetic resonance imaging has been used for this purpose, cranial ultrasound (cUS) is the primary imaging technique used in clinical practice. Here, we present the first study aiming to aut...

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Autores: Regalado, María, Carreras Blesa, Nuria, Mata Miquel, Christian, Oliver i Malagelada, Arnau, Lladó Bardera, Xavier, Agut, Thais
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos: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:10256/26383
Acesso em linha:http://hdl.handle.net/10256/26383
Access Level:acceso abierto
Palavra-chave:Imatges -- Segmentació
Image segmentation
Imatgeria mèdica
Imaging systems in medicine
Ecoencefalografia
Ultrasonic encephalography
Aprenentatge profund
Deep learning
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repository_id_str
spelling Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning ModelsRegalado, MaríaCarreras Blesa, NuriaMata Miquel, ChristianOliver i Malagelada, ArnauLladó Bardera, XavierAgut, ThaisImatges -- SegmentacióImage segmentationImatgeria mèdicaImaging systems in medicineEcoencefalografiaUltrasonic encephalographyAprenentatge profundDeep learningObjective: Segmentation of brain sulci in pre-term infants is crucial for monitoring their development. While magnetic resonance imaging has been used for this purpose, cranial ultrasound (cUS) is the primary imaging technique used in clinical practice. Here, we present the first study aiming to automate brain sulci segmentation in pre-term infants using ultrasound images. Methods: Our study focused on segmentation of the Sylvian fissure in a single cUS plane (C3), although this approach could be extended to other sulci and planes. We evaluated the performance of deep learning models, specifically U-Net and ResU-Net, in automating the segmentation process in two scenarios. First, we conducted cross-validation on images acquired from the same ultrasound machine. Second, we applied fine-tuning techniques to adapt the models to images acquired from different vendors. Results: The ResU-Net approach achieved Dice and Sensitivity scores of 0.777 and 0.784, respectively, in the cross-validation experiment. When applied to external datasets, results varied based on similarity to the training images. Similar images yielded comparable results, while different images showed a drop in performance. Additionally, this study highlighted the advantages of ResU-Net over U-Net, suggesting that residual connections enhance the model's ability to learn and represent complex anatomical structures. Conclusion: This study demonstrated the feasibility of using deep learning models to automatically segment the Sylvian fissure in cUS images. Accurate sonographic characterisation of cerebral sulci can improve the understanding of brain development and aid in identifying infants with different developmental trajectories, potentially impacting later functional outcomesThis work was supported by grant no. PI18/00110 from the Instituto de Salud Carlos III, co-funded by the European Regional Development FundOpen Access funding provided thanks to the CRUE-CSIC agreement with ElsevierElsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/26383Ultrasound in Medicine & Biology, 2025, vol. 51, núm. 3, p. 543-550Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ultrasmedbio.2024.11.016info:eu-repo/semantics/altIdentifier/issn/0301-5629info:eu-repo/semantics/altIdentifier/eissn/1879-291XAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/263832026-05-29T05:05:01Z
dc.title.none.fl_str_mv Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
title Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
spellingShingle Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
Regalado, María
Imatges -- Segmentació
Image segmentation
Imatgeria mèdica
Imaging systems in medicine
Ecoencefalografia
Ultrasonic encephalography
Aprenentatge profund
Deep learning
title_short Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
title_full Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
title_fullStr Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
title_full_unstemmed Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
title_sort Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models
dc.creator.none.fl_str_mv Regalado, María
Carreras Blesa, Nuria
Mata Miquel, Christian
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
Agut, Thais
author Regalado, María
author_facet Regalado, María
Carreras Blesa, Nuria
Mata Miquel, Christian
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
Agut, Thais
author_role author
author2 Carreras Blesa, Nuria
Mata Miquel, Christian
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
Agut, Thais
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Imatges -- Segmentació
Image segmentation
Imatgeria mèdica
Imaging systems in medicine
Ecoencefalografia
Ultrasonic encephalography
Aprenentatge profund
Deep learning
topic Imatges -- Segmentació
Image segmentation
Imatgeria mèdica
Imaging systems in medicine
Ecoencefalografia
Ultrasonic encephalography
Aprenentatge profund
Deep learning
description Objective: Segmentation of brain sulci in pre-term infants is crucial for monitoring their development. While magnetic resonance imaging has been used for this purpose, cranial ultrasound (cUS) is the primary imaging technique used in clinical practice. Here, we present the first study aiming to automate brain sulci segmentation in pre-term infants using ultrasound images. Methods: Our study focused on segmentation of the Sylvian fissure in a single cUS plane (C3), although this approach could be extended to other sulci and planes. We evaluated the performance of deep learning models, specifically U-Net and ResU-Net, in automating the segmentation process in two scenarios. First, we conducted cross-validation on images acquired from the same ultrasound machine. Second, we applied fine-tuning techniques to adapt the models to images acquired from different vendors. Results: The ResU-Net approach achieved Dice and Sensitivity scores of 0.777 and 0.784, respectively, in the cross-validation experiment. When applied to external datasets, results varied based on similarity to the training images. Similar images yielded comparable results, while different images showed a drop in performance. Additionally, this study highlighted the advantages of ResU-Net over U-Net, suggesting that residual connections enhance the model's ability to learn and represent complex anatomical structures. Conclusion: This study demonstrated the feasibility of using deep learning models to automatically segment the Sylvian fissure in cUS images. Accurate sonographic characterisation of cerebral sulci can improve the understanding of brain development and aid in identifying infants with different developmental trajectories, potentially impacting later functional outcomes
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/26383
url http://hdl.handle.net/10256/26383
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ultrasmedbio.2024.11.016
info:eu-repo/semantics/altIdentifier/issn/0301-5629
info:eu-repo/semantics/altIdentifier/eissn/1879-291X
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Ultrasound in Medicine & Biology, 2025, vol. 51, núm. 3, p. 543-550
Articles publicats (D-ATC)
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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score 15,81155

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