ABSTRACT
Ultra high-field MRI enables sub-millimetre resolution imaging of the human brain, allowing for the resolution of functional circuits at the meso-scale of cortical layers. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR brain images to delineate anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks of accurate and fast methods for segmenting 7 Tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end Convolutional Neural Network (CNN) architecture, that allows for the segmentation of a whole 7T T1w MRI brain volume at once, thus overcoming the drawbacks of partitioning the volume into 2D or 3D tiles. Training is performed in a weakly supervised fashion, exploiting labelling with errors obtained with automatic state-of-the-art methods. The trained model is able to produce accurate multi-structure segmentation masks on six different classes in only a few seconds. In the experimental part, a combination of objective numerical evaluations and subjective analysis carried out by experienced neuroimaging users, confirms that the proposed solution outperforms the training labels it was trained on in segmentation accuracy, and is suitable for neuroimaging studies, such as layer fMRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to efficiently and effectively apply CEREBRUM-7T to data from different sites. Furthermore, to allow replicability and encourage extensions, we release the code, 7T data (142 volumes), and other materials, including the training labels and the Turing test.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
* Release of a dedicated website for sharing project content (data, code, results). * Additional section on Fine-Tuning, to extend the work on new datasets * Update the paper
