Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10, 10200 (2020). https://doi.org/10.1038/s41598-020-67076-5
A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician (B.V-A.). Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients.
Images are in ./Images/*.png
All information related with the images is in FETAL_PLANES_DB_data (provided both in csv and xlsx formats)
The dataset details are described in our open-acces paper: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
If you find this dataset useful, please cite:
@article{Burgos-ArtizzuFetalPlanesDataset,
title={Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes},
author={Burgos-Artizzu, X.P. and Coronado-Gutiérrez, D. and Valenzuela-Alcaraz, B. and Bonet-Carne, E. and Eixarch, E. and Crispi, F. and Gratacós, E.},
journal={Nature Scientific Reports},
volume={10},
pages={10200},
doi="10.1038/s41598-020-67076-5",
year={2020}
}
bash ./download.sh