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This dataset contains de-identified maternal-fetal ultrasound images released under CC BY 4.0. Access is granted for research and educational use. By requesting access you agree to cite the original authors and not to attempt re-identification of any subject.
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Fetal Planes DB
A mirror of FETAL_PLANES_DB: 12,400 routinely-acquired maternal-fetal screening ultrasound images from two hospitals, captured by several operators on several machines and manually labelled by an expert maternal-fetal clinician.
Contents
| Field | Description |
|---|---|
image |
The ultrasound image |
Plane |
Abdomen, Brain, Femur, Thorax, Maternal cervix, Other |
Brain_plane |
Trans-thalamic, Trans-cerebellum, Trans-ventricular, Other, Not A Brain |
Patient_num |
Subject id — group by this when splitting |
Train |
1 = train, 0 = test (the authors' official, patient-disjoint split) |
US_Machine, Operator |
Acquisition metadata |
Splits follow the original Train column.
Usage
from datasets import load_dataset
ds = load_dataset("shr3m/fetal-planes-db") # requires access approval
Notes
- Classes are imbalanced;
Otheris a large catch-all that the FetalCLIP zero-shot benchmark excludes. - Images vary in size and are mostly non-square — pad to square before resizing, do not stretch.
- The official split is patient-disjoint. Preserve that if you re-split.
Licence and citation
CC BY 4.0. Attribution is required. This is a mirror; all credit belongs to the original authors.
Original dataset: DOI 10.5281/zenodo.3904280
@article{burgos2020evaluation,
title = {Evaluation of deep convolutional neural networks for automatic
classification of common maternal fetal ultrasound planes},
author = {Burgos-Artizzu, Xavier P. and Coronado-Guti{\'e}rrez, David and
Valenzuela-Alcaraz, Brenda and Bonet-Carne, Elisenda and
Eixarch, Elisenda and Crispi, Fatima and Gratac{\'o}s, Eduard},
journal = {Scientific Reports},
volume = {10},
number = {1},
pages = {10200},
year = {2020},
doi = {10.1038/s41598-020-67076-5}
}
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