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

Visit the [project website](https://rocknroll87q.github.io/cerebrum7t/) 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

Note: if you are testing the model with these data, please notice that you need to download the `mean` and `std` volumes [at this link](https://cloud.psy.gla.ac.uk/index.php/s/efPCRdOB6FCEzrT) (psw: `rocknroll87q/cerebrum7t`).

Project: [link](https://rocknroll87q.github.io/cerebrum7t/)
Code: [link](https://github.com/rockNroll87q/cerebrum7t)
Paper: [link](https://www.biorxiv.org/content/10.1101/2020.07.07.191536v2)
Full dataset: [link](https://search.kg.ebrains.eu/instances/Dataset/2b24466d-f1cd-4b66-afa8-d70a6755ebea)