FeTA Dataset: Fetal Tissue Annotations dataset for machine-learning and deep-learning based image segmentation algorithm development (research use).
The Fetal Tissue Annotation and Segmentation Dataset (FeTA) is part of an initiative led by the University Children's Hospital Zürich and the University of Zürich. The dataset facilitates the development of novel machine-learning and deep-learning based multi-class segmentation methods for the quantification of brain development on fetal MRI. The ultimate goal is to capture pathological developmental trajectories by the automated quantification of the prenatal development, for which automated approaches free of observer bias are indispensable.
For the dataset of the Fetal Brain Tissue Annotation and Segmentation Challenge, MICCAI 2021, please visit: https://www.synapse.org/#!Synapse:syn25649159/wiki/610007 and https://feta-2021.grand-challenge.org/
Each fetal brains were labeled for 7 tissue categories: cortex, white matter, external CSF spaces, ventricle system, deep gray matter, cerebellum and brainstem.
Link to the Annotation Guideline
Collaborators:
Center for MR-Research, University Children's Hospital Zürich, Switzerland
Brain Research Institute, University of Zürich, Switzerland
MIALS Lab, CHUV/University of Lausanne, Switzerland
Technical University of München
University of Debrecen, Hungary