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Z\u00fcrich" } ], "person_or_org": { "family_name": "Payette", "given_name": "Kelly", "name": "Payette, Kelly", "type": "personal" } }, { "affiliations": [ { "name": "Center for MR-Research, University Children's Hospital Z\u00fcrich" } ], "person_or_org": { "family_name": "Jakab", "given_name": "Andras", "name": "Jakab, Andras", "type": "personal" } } ], "description": "
The Fetal Tissue Annotation Dataset (FeTA) consist of manually annotated, T2-weighted, super-resolution reconstructed fetal cerebral magnetic resonance images. It is a mixture of normally developing cases and pathologies. The dataset is a valuable source for developing automated image segmentation algorithms as it provides open source MRI data and expert manual annotations, which is a particularly time consuming process. Each fetal brains were labeled for 7 tissue categories: grey matter, white matter, external CSF spaces, ventricle system, deep gray matter, cerebellum and brainstem.
\n\nFrom May 2021, access to the FeTA dataset is only possible on the Synapse platform. We released the second version with 80 cases, which must be used for participants of the MICCAI Fetal Tissue Annotation Challenge in 2021. Please visit the following sites for further information:
\n\nhttps://feta-2021.grand-challenge.org/
\n\nhttps://www.synapse.org/#!Synapse:syn25649159/wiki/610007
\n\nBackground
\n\nCongenital disorders are one of the leading causes of infant mortality worldwide. Recently, fetal MRI has started to emerge as a valuable tool for investigating the neurological development of fetuses with congenital disorders in order to aid in prenatal planning. Moreover, fetal MRI is a powerful tool to portray the complex neurodevelopmental events during human gestation, which remain to be completely characterized. Automated segmentation and quantification of the highly complex and rapidly changing brain morphology in MRI data would improve the diagnostic process, as manual segmentation is both time consuming and prone to human error and inter-rater variability. The automatic segmentation of the developing human brain would be a first step in being able to perform such an analysis. The FeTA Dataset and the Challenge we plan to organize are important steps in the development of reproducible methods of analyzing high resolution MR images of the developing fetal brain. Such new algorithms will have the potential to better understand the underlying causes of congenital disorders and ultimately to support decision-making and prenatal planning.
\n\n", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2021-02-15", "publisher": "Zenodo", "related_identifiers": [ { "identifier": "https://arxiv.org/abs/2010.15526", "relation_type": { "id": "isdocumentedby", "title": { "de": "Wird dokumentiert von", "en": "Is documented by" } }, "resource_type": { "id": "publication-preprint", "title": { "de": "Preprint", "en": "Preprint" } }, "scheme": "url" }, { "identifier": "https://arxiv.org/abs/2010.12391", "relation_type": { "id": "iscitedby", "title": { "de": "Wird zitiert von", "en": "Is cited by" } }, "resource_type": { "id": "publication-preprint", "title": { "de": "Preprint", "en": "Preprint" } }, "scheme": "url" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "subjects": [ { "subject": "fetal" }, { "subject": "machine learning" }, { "subject": "image segmentation" }, { "subject": "MRI" }, { "subject": "brain development" }, { "subject": "deep learning" }, { "subject": "magnetic resonance imaging" }, { "subject": "fetus" }, { "subject": "brain" } ], "title": "Fetal Tissue Annotation\u00a0Dataset FeTA", "version": "1.2.1" }, "parent": { "access": { "owned_by": { "user": 197222 }, "settings": { "accept_conditions_text": "
TERMS OF USE
\n\nThis is an agreement (“Agreement”) between you the downloader (“Downloader”) and the owner of the materials (“User”) governing the use of the Fetal Tissue Annotation Dataset ("Materials") to be downloaded.
\n\nI. Acceptance of this Agreement
\n\nBy downloading or otherwise accessing the Materials, the Downloader represents his/her acceptance of the terms of this Agreement.
\n\nII. Data ownership
\n\nThe owner of the Materials is the University Children’s Hospital Zurich.
\n\nIII. Use of the Materials
\n\nMaterials is used for research and education. Any other kind of use you will lead to recall of all datasets, stop of collaboration and legal consequences.
\n\nThis Agreement represents the entire agreement between Downloader and User with respect to the downloading and use of the Materials, and supersedes all prior or contemporaneous communications and proposals (whether oral, written or electronic) between Downloader and User with respect to downloading or using the Materials.
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