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 M, Carreras N, Mata C, Oliver A, Lladó X, Agut T
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p27280
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=27280
Access Level:acceso abierto
Palabra clave:Brain sulci
Deep learning
Pre-term infants
Segmentation
Sylvian fissure
Ultrasonic imaging
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spelling Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models.Regalado MCarreras NMata COliver ALladó XAgut TBrain sulciDeep learningPre-term infantsSegmentationSylvian fissureUltrasonic imagingOBJECTIVE: 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.ELSEVIER SCIENCE INC2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=27280ULTRASOUND IN MEDICINE AND BIOLOGYISSN: 03015629ISSNe: 1879291Xreponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p272802026-05-27T12:37:41Z
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 M
Brain sulci
Deep learning
Pre-term infants
Segmentation
Sylvian fissure
Ultrasonic imaging
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 M
Carreras N
Mata C
Oliver A
Lladó X
Agut T
author Regalado M
author_facet Regalado M
Carreras N
Mata C
Oliver A
Lladó X
Agut T
author_role author
author2 Carreras N
Mata C
Oliver A
Lladó X
Agut T
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Brain sulci
Deep learning
Pre-term infants
Segmentation
Sylvian fissure
Ultrasonic imaging
topic Brain sulci
Deep learning
Pre-term infants
Segmentation
Sylvian fissure
Ultrasonic imaging
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
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url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=27280
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 ELSEVIER SCIENCE INC
publisher.none.fl_str_mv ELSEVIER SCIENCE INC
dc.source.none.fl_str_mv ULTRASOUND IN MEDICINE AND BIOLOGY
ISSN: 03015629
ISSNe: 1879291X
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
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