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This is the first publicly available dataset of in vivo fetoscopic videos with placental vessel annotations.
Overview
The fetoscopy placenta dataset is associated with our MICCAI2020 publication titled “Deep Placental Vessel Segmentation for Fetoscopic Mosaicking”. The dataset contains 483 frames with ground-truth vessel segmentation annotations taken from six different in vivo fetoscopic procedure videos. The dataset also includes six unannotated in vivo continuous fetoscopic video clips (950 frames) with predicted vessel segmentation maps obtained from the leave-one-out cross validation of our method.
For ground-truth vessel annotation, we selected the non-occluded (no fetus or tool presence) frames through a separate frame-level fetoscopic event identification approach Bano:IJCARS2020. We annotate a binary mask for vessel segmentation using the Pixel Annotation Tool.
Downloading the Dataset
This dataset has been released. If you wish to download this dataset, please check here.
License
The Fetoscopy Placenta Dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Citing the Dataset
Please cite the following publication [Bano:MICCAI2020] whenever research making use of this dataset is reported in any academic publication or research report:
@inproceedings{bano2020vessel,
title={Deep Placental Vessel Segmentation for Fetoscopic Mosaicking},
author={Bano, Sophia and Vasconcelos, Francisco and Shepherd, Luke M. and Vander Poorten, Emmanue and Vercauteren, Tom and Ourselin, Sebastien and David, Anna L. and Deprest, Jan and Stoyanov, Danail},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
year={2020},
organization={Springer}
}
The following publications are also associated with this work.
[1] S. Bano, F. Vasconcelos, M. Tella Amo, G. Dwyer, C. Gruijthuijsen, J. Deprest, S. Ourselin, E. Vander Poorten, T. Vercauteren, D. Stoyanov, Deep sequential mosaicking of fetoscopic videos, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 311–319, Springer (2019) [CrossRef]
[2] S. Bano, F. Vasconcelos, E. Vander Poorten, T. Vercauteren, S. Ourselin, J. Deprest, D. Stoyanov, FetNet: A recurrent convolutional network for occlusion identification in fetoscopic videos, International Journal of Computer Assisted Radiology and Surgery, 15(5), 791–801 (2020) [CrossRef]
Contact
For comments, suggestions or feedback, or if you experience any problems with this website or the dataset, please contact Sophia Bano.
To find out more about our research team, visit the Surgical Robot Vision and WELLCOME/EPSRC Centre for Interventional and Surgical Science websites.