2026-06-29T11:59:58Z https://api.figshare.com/v2/oai
oai:figshare.com:article/30536171 2025-11-05T11:51:58Z category_28879 category_28891 category_28930 category_29161 portal_549 item_type_3 month_year_11_2025
10.5522/04/30536171.v1 https://figshare.com/articles/dataset/Fetoscopy_Placenta_Dataset/30536171 https://ndownloader.figshare.com/files/59303753 https://ndownloader.figshare.com/files/59303756 https://ndownloader.figshare.com/files/59303759 10.1007/978-3-030-59716-0_73 Bano, Sophia Sophia Bano Vasconcelos, Francisco Francisco Vasconcelos M. Shepherd, Luke Luke M. Shepherd Vander Poorten, Emmanuel Emmanuel Vander Poorten Vercauteren, Tom Tom Vercauteren Ourselin, Sebastien Sebastien Ourselin David, Anna L. Anna L. David Deprest, Jan Jan Deprest Stoyanov, Danail Danail Stoyanov <![CDATA[Fetoscopy Placenta Dataset]]> Computer vision Pattern recognition Data engineering and data science Deep learning Fetoscopy Deep learning Vessel segmentation Vessel registration twin-to-twin transfusion syndrome Mosaicking 2025-11-05 2025-11-05 Dataset 2025 University College London First Fetoscope Placenta Dataset for In-Vivo Vessel Segmentation and Mosaicking

Overview

The fetoscope placenta dataset described in [Bano et al. MICCAI2020] consists of in-vivo fetoscopic imagery from twin-to-twin transfusion syndrome (TTTS) procedures. It includes:

  • 483 frames from 6 fetoscopic videos with expert ground-truth placental vessel segmentation annotations.
  • 6 additional continuous fetoscopic video clips (total 950 frames) without manual labels but accompanied by predicted vessel segmentation maps generated via leave-one-out cross-validation.

The dataset is intended for research on placental vessel segmentation and fetoscopic video mosaicking/registration under realistic surgical conditions (limited field of view, specularities, and variable visibility).

Citing the Dataset

Cite [Bano et al. MICCAI2020] whenever research making use of this dataset is reported in any academic publication or research report.

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