CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes

uploaded by Michele Svanera on 2021-04-27 - 6 months ago
last modified on 2021-04-27 - 6 months ago
authored by Michele Svanera, Lars Muckli
3543431

OpenNeuro Accession Number: ds003642
Files: 9, Size: 103.98MB, Subjects: 3, Sessions: 3
No Available Tasks
Available Modalities: MRI

README

Visit the project website for more.

The dataset is composed by 3 subjects, scanned at the Imaging Centre of Excellence (ICE) at the Queen Elizabeth University Hospital, Glasgow (UK). The full database consists of 142 out-of-the-scanner volumes obtained with a MP2RAGE sequence at 0.63 mm3 isotropic resolution, using a 7-Tesla MRI scanner with 32-channel head coil. These data serve as testing dataset for the paper:

Svanera, M., Benini, S., Bontempi, D., & Muckli, L. (2020). 
CEREBRUM-7T: fast and fully-volumetric brain segmentation of out-of-the-scanner 7T MR volumes. 
bioRxiv.

For every subject, two folders are provided, containing:

anat/

  • INV1
  • INV2
  • UNI_Images (T1w)

derivatives/

  • manual annotations of 8 regions of widely interest in neuroscience early visual cortex (EVC) high-level visual areas (HVC) motor cortex (MCX) cerebellum (CER) hippocampus (HIP) early auditory cortex (EAC) brainstem (BST) basal ganglia (BGA)
  • automatic segmentation by FreeSurfer (v6 and v7)
  • automatic segmentation by Fracasso16
  • automatic segmentation by our method (CEREBRUM7T) with probability maps (CEREBRUM7T_probMap)
  • automatic segmentation by nighres
  • labels used for training our method

Project: link Code: link Paper: link Full dataset: link

Authors

  • Michele Svanera
  • Lars Muckli

Dataset DOI

10.18112/openneuro.ds003642.v1.1.0

License

CC0

Acknowledgements

How to Acknowledge

Svanera, M., Benini, S., Bontempi, D., & Muckli, L. (2020). CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes. bioRxiv.

Funding

  • This project has received funding from the European Union Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 and 945539 (Human Brain Project SGA2 and SGA3).

References and Links

Ethics Approvals

How To Cite

Copy
Michele Svanera and Lars Muckli (2021). CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003642.v1.1.0
More citation info

CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes

uploaded by Michele Svanera on 2021-04-27 - 6 months ago
last modified on 2021-04-27 - 6 months ago
authored by Michele Svanera, Lars Muckli
3543431

OpenNeuro Accession Number: ds003642
Files: 9, Size: 103.98MB, Subjects: 3, Sessions: 3
No Available Tasks
Available Modalities: MRI

README

Visit the project website for more.

The dataset is composed by 3 subjects, scanned at the Imaging Centre of Excellence (ICE) at the Queen Elizabeth University Hospital, Glasgow (UK). The full database consists of 142 out-of-the-scanner volumes obtained with a MP2RAGE sequence at 0.63 mm3 isotropic resolution, using a 7-Tesla MRI scanner with 32-channel head coil. These data serve as testing dataset for the paper:

Svanera, M., Benini, S., Bontempi, D., & Muckli, L. (2020). 
CEREBRUM-7T: fast and fully-volumetric brain segmentation of out-of-the-scanner 7T MR volumes. 
bioRxiv.

For every subject, two folders are provided, containing:

anat/

  • INV1
  • INV2
  • UNI_Images (T1w)

derivatives/

  • manual annotations of 8 regions of widely interest in neuroscience early visual cortex (EVC) high-level visual areas (HVC) motor cortex (MCX) cerebellum (CER) hippocampus (HIP) early auditory cortex (EAC) brainstem (BST) basal ganglia (BGA)
  • automatic segmentation by FreeSurfer (v6 and v7)
  • automatic segmentation by Fracasso16
  • automatic segmentation by our method (CEREBRUM7T) with probability maps (CEREBRUM7T_probMap)
  • automatic segmentation by nighres
  • labels used for training our method

Project: link Code: link Paper: link Full dataset: link

Authors

  • Michele Svanera
  • Lars Muckli

Dataset DOI

10.18112/openneuro.ds003642.v1.1.0

License

CC0

Acknowledgements

How to Acknowledge

Svanera, M., Benini, S., Bontempi, D., & Muckli, L. (2020). CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes. bioRxiv.

Funding

  • This project has received funding from the European Union Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 and 945539 (Human Brain Project SGA2 and SGA3).

References and Links

Ethics Approvals

How To Cite

Copy
Michele Svanera and Lars Muckli (2021). CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003642.v1.1.0
More citation info

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