PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research

This article presents a curated dataset of transperineal pelvic floor ultrasound videos collected from 111 patients in a clinical setting using a Canon i700 Aplio® ultrasound system with a PVT-675 MV 3D probe. Each video captures the midsagittal view of pelvic floor organs at rest and during the Val...

Descripción completa

Detalles Bibliográficos
Autores: Solís Martín, David, Sáinz Bueno, José Antonio, Borrego Díaz, Joaquín
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/180307
Acceso en línea:https://hdl.handle.net/11441/180307
https://doi.org/10.1016/j.dib.2025.112346
Access Level:acceso abierto
Palabra clave:Pelvic floor
Dataset
Segmentation
Computer visión
Deep learning
Medical images
id ES_c8d9ba8bee11a2225dbd0e8a9097a00a
oai_identifier_str oai:idus.us.es:11441/180307
network_acronym_str ES
network_name_str España
repository_id_str
spelling PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing researchSolís Martín, DavidSáinz Bueno, José AntonioBorrego Díaz, JoaquínPelvic floorDatasetSegmentationComputer visiónDeep learningMedical imagesThis article presents a curated dataset of transperineal pelvic floor ultrasound videos collected from 111 patients in a clinical setting using a Canon i700 Aplio® ultrasound system with a PVT-675 MV 3D probe. Each video captures the midsagittal view of pelvic floor organs at rest and during the Valsalva maneuver. Eight anatomical structures were manually annotated by an expert sonographer using the CVAT platform, resulting in pixel-level segmentation masks. The dataset is intended to support research in automated pelvic floor assessment, medical image segmentation, and dynamic organ tracking. To facilitate reuse, a public source code repository is provided with scripts for data loading, mask generation, and training of baseline deep learning models, including Feature Pyramid Networks (FPNs). This dataset represents the first annotated ultrasound video resources focused on pelvic floor anatomy and is designed to enable benchmarking, reproducibility, and methodological innovation in computer-assisted diagnosis and medical image analysis.ElsevierCirugíaCiencias de la Computación e Inteligencia ArtificialTIC137: Lógica, Computación e Ingeniería del Conocimiento2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/180307https://doi.org/10.1016/j.dib.2025.112346reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésData in Brief, 64, 112346.https://www.sciencedirect.com/science/article/pii/S2352340925010601?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1803072026-06-17T12:51:07Z
dc.title.none.fl_str_mv PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
title PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
spellingShingle PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
Solís Martín, David
Pelvic floor
Dataset
Segmentation
Computer visión
Deep learning
Medical images
title_short PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
title_full PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
title_fullStr PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
title_full_unstemmed PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
title_sort PFUS1: Premier pelvic floor ultrasound segmentation dataset. A resource for advancing research
dc.creator.none.fl_str_mv Solís Martín, David
Sáinz Bueno, José Antonio
Borrego Díaz, Joaquín
author Solís Martín, David
author_facet Solís Martín, David
Sáinz Bueno, José Antonio
Borrego Díaz, Joaquín
author_role author
author2 Sáinz Bueno, José Antonio
Borrego Díaz, Joaquín
author2_role author
author
dc.contributor.none.fl_str_mv Cirugía
Ciencias de la Computación e Inteligencia Artificial
TIC137: Lógica, Computación e Ingeniería del Conocimiento
dc.subject.none.fl_str_mv Pelvic floor
Dataset
Segmentation
Computer visión
Deep learning
Medical images
topic Pelvic floor
Dataset
Segmentation
Computer visión
Deep learning
Medical images
description This article presents a curated dataset of transperineal pelvic floor ultrasound videos collected from 111 patients in a clinical setting using a Canon i700 Aplio® ultrasound system with a PVT-675 MV 3D probe. Each video captures the midsagittal view of pelvic floor organs at rest and during the Valsalva maneuver. Eight anatomical structures were manually annotated by an expert sonographer using the CVAT platform, resulting in pixel-level segmentation masks. The dataset is intended to support research in automated pelvic floor assessment, medical image segmentation, and dynamic organ tracking. To facilitate reuse, a public source code repository is provided with scripts for data loading, mask generation, and training of baseline deep learning models, including Feature Pyramid Networks (FPNs). This dataset represents the first annotated ultrasound video resources focused on pelvic floor anatomy and is designed to enable benchmarking, reproducibility, and methodological innovation in computer-assisted diagnosis and medical image analysis.
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
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/180307
https://doi.org/10.1016/j.dib.2025.112346
url https://hdl.handle.net/11441/180307
https://doi.org/10.1016/j.dib.2025.112346
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Data in Brief, 64, 112346.
https://www.sciencedirect.com/science/article/pii/S2352340925010601?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869419321085657088
score 15,81155

Similares en LA Referencia