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...
| Authors: | ; ; |
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| Format: | Article |
| Status: | Published version |
| Publication date: | 2025 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/180307 |
| Online Access: | https://hdl.handle.net/11441/180307 https://doi.org/10.1016/j.dib.2025.112346 |
| Access Level: | Open access |
| Keyword: | Pelvic floor Dataset Segmentation Computer visión Deep learning Medical images |
| Summary: | 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. |
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