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First publicly available largescale multi-centre dataset of in vivo fetoscopic videos with placental scene semantic annotations from the FetReg2021 –Endoscopic Vision challenge organised at MICCAI2021
Overview
Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge. Through FetReg2021 challenge, we released the first large-scale multi-centre dataset of fetoscopy laser photocoagulation procedure. The dataset contains 2,718 pixel-wise annotated images (for background, vessel, fetus, tool classes) from 24 different in vivo TTTS fetoscopic surgeries and 24 unannotated video clips video clips containing 9,616 frames for training and testing. The dataset is useful for the development of generalized and robust semantic segmentation and video mosaicking algorithms for long duration fetoscopy videos.
Further details about this dataset are provided in our dataset description [Bano:arXiv2021]and challenge analysis [Bano:arXiv2022] publications. Please visit the FetReg2021 challenge website for more information about the challenge.
FetReg2021 challenge was featured as the Challenge of the Month in the Computer Vision News June 2021 issue.
Downloading the Dataset
The complete FetReg2021 dataset from 24 in vivo TTTS fetoscopic surgeries have been released. If you wish you download this dataset, please check HERE.
License
The FetReg2021 Challenge 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 publications [Bano:arXiv2021, Bano:arXiv2022] whenever research making use of this dataset is reported in any academic publication or research report:
@article{bano2021fetreg,
title={FetReg: placental vessel segmentation and registration in fetoscopy challenge dataset},
author={Bano, Sophia and Casella, Alessandro and Vasconcelos, Francisco and Moccia, Sara and Attilakos, George and Wimalasundera, Ruwan and David, Anna L and Paladini, Dario and Deprest, Jan and De Momi, Elena and Mattos, Leonardo S and Stoyanov, Danail},
journal={arXiv preprint arXiv:2106.05923},
year={2021}
}
@article{bano2022fetreg2021,
title={FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy},
author={Bano, Sophia and Casella, Alessandro and Vasconcelos, Francisco and Qayyum, Abdul and Benzinou, Abdesslam and Mazher, Moona and Meriaudeau, Fabrice and Lena, Chiara and Cintorrino, Ilaria Anita and De Paolis, Gaia Romana and others},
journal={arXiv preprint arXiv:2206.12512},
year={2022}
}
The following publications are also associated with this work.
[1] Bano, S., Vasconcelos, F., Shepherd, L.M., Poorten, E.V., Vercauteren, T., Ourselin, S., David, A.L., Deprest, J. and Stoyanov, D., 2020, October. Deep placental vessel segmentation for fetoscopic mosaicking. International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 763-773). Springer, Cham [CrossRef][Github]
[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 (sophia.bano@ucl.ac.uk).
To find out more about our research team, visit the Surgical Robot Vision and WELLCOME / EPSRC Centre for Interventional and Surgical Science websites.
