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Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation This repository contains data contributed and used in our MICCAI 2021 paper: Fidon, L. et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. Our code is publicly available at <a href="https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21">https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21</a> Files: <ul> <li>FeTA2021_Release1and2Corrected_v4.zip: contains 90 fetal brain T2w MRIs of the FeTA 2021 dataset with updated manual segmentations</li> <li>MICCAI21_partial_supervision_trained_models.zip: contains the weights of our pre-trained deep neural networks for fetal brain 3D MRI segmentation</li> </ul> The FeTA 3D MRIs and segmentations contained in this repository shall be used only for research and education purposes. Segmentation labels: 1: white matter (excluding corpus callosum; see below) 2: intra-axial cerebrospinal fluid (CSF) 3: cerebellum 4: extra-axial CSF 5: cortical gray matter 6: deep gray matter 7: brainstem 8: corpus callosum Updates: Version 4 (November 2022) 8 cases were forgotten by mistake when uploading version 3 (sub-feta004, sub-feta008, sub-feta017, sub-feta020, sub-feta021, sub-feta022, sub-feta023, sub-feta027). I have added them back in version 4, including the corpus callosum segmentations for those cases. Thank you to those who let me know about this. Please do not hesitate to reach out to me (lucas.fdon@gmail.com) if you think something is wrong. Version 3 (July 2022) Added some corpus callosum segmentations that were missing in version 2. Version 2 (Avril 2022) 3D MRI and refined manual segmentation have been added for: - the 10 fetal brain MRIs of the testing set of the FeTA data release 1 (sub-feta081 to sub-feta090). - the 40 fetal brain MRIs new in the FeTA data release 2 (sub-041 to sub080). The same pre-processing as in version 1 below has been performed. The code used for the pre-processing is available at https://github.com/LucasFidon/fetal-seg-preprocessing Some additional refinements of the manual segmentation have been performed for sub-feta001 to sub-feta040. See participant.tsv for more information about the 3D MRI studies. The pathology for each study has been diagnosed by two radiologists in our team and can be found in participant.tsv. Those are not present in the original FeTA dataset release 2.1 Version 1 Summary of the changes done on the original FeTA dataset (data release 1) [1]. The only modifications we did are detailed below (pre-processing and manual segmentation). Pre-processing: -registration to the neurotypical fetal brain atlas of Gholipour et al [2] -resampling to 0.8mm isotropic -automatic mask via label propagation using a neurotypical [2] and a spina bifida [3] fetal brain atlas. The code used for the pre-processing is available at https://github.com/LucasFidon/fetal-seg-preprocessing Segmentation: Manual corrections of all manual segmentations by Michael Aertsen, Lucas Fidon, and Philippe Demaerel for the 38 3D MRIs in the FeTA dataset release 1. For 2 cases, the segmentations were not corrected manually (see /Excluded). [1] 10.5281/zenodo.4541605 [2] http://crl.med.harvard.edu/research/fetal_brain_atlas/ [3] 10.7303/syn25887675 How to cite: If you use the deep neural network weights please cite: <ul> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> if you use the FeTA data (release 1 and 2) and the corrected segmentations that we contributed please cite: <ul> <li>Payette, K., de Dumast, P., Kebiri, H. et al. An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Sci Data 8, 167 (2021). https://doi.org/10.1038/s41597-021-00946-3</li> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> Terms of use - FeTA 3D MRIs and segmentations: This is an agreement (“Agreement”) between you the downloader (“Downloader”) and the owner of the materials (“User”) governing the use of the Fetal Tissue Annotation and Segmentation Dataset to be downloaded. I. Acceptance of this Agreement By downloading or otherwise accessing the Fetal Tissue Annotation and Segmentation Dataset, the Downloader represents his/her acceptance of the terms of this Agreement. II. Data ownership The owner of the Fetal Tissue Annotation and Segmentation Dataset is the University Children’s Hospital Zurich. III. Use of the Materials Fetal Tissue Annotation and Segmentation Dataset is used only for research and education. Any other kind of use you will lead to the recall of all datasets, stop of collaboration, and legal consequences. This 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.

restrictedJul 2021View details →

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Discard data set

N/A

opencc-by-4.0Jan 2023View details →

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Cattle SNP set

Cattle SNP VCF file

opencc-by-4.0Feb 2023View details →

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Common Knowledge Processing Patterns in Networks of Different Systems Minimal Data Set

Common Knowledge Processing Patterns in Networks of Different Systems Minimal Data Set

opencc-by-4.0Jul 2023View details →

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Data set networks for water protection

The data set includes data collected with collaborative networks for water protection in Brazil.

opencc-by-4.0Jul 2023View details →

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BIM and CAD Data sets for Large Scale Pilots

Datasets for large-scale demonstration in the form digital plans including BIM are given in this object.

restrictedSep 2020View details →

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Data set for journal paper 'Mode I fracture of thick adhesively bonded GFRP composite joints for wind turbine rotor blades' volume2

The description in the read me file is updated here. This data set includes all the related raw data about the journal paper 'Mode I fracture of thick adhesively bonded GFRP composite joints for wind turbine rotor blades' 1.The raw data of thick adhesive double cantilever beam tests under quasi-static loading, such as the DIC pictures and the load-displacement curve. The strain energy release curve and load-displacement prediction curve are also included. All the data excepts the DIC pictures is within one Excel file for each sample. 2.Data for tensile testing (composite and epoxy adhesive). Volume 1 includes the data of UN1,UN2,GN2 and GL2-L. Volume 2 includes the data of 5-GL2-M SAMPLE1. Volume 3 includes the data of 5-GL2-M SAMPLE2 AND 3. Volume 4 includes the data of 5-GL2-M SAMPLE4 and 6-GL2-H SAMPLE 1 and 2. Volume 5 includes the data of 6-GL2-H SAMPLE3 and 4. Volume 6 includes the tensile testing data.