  This paper introduces a novel multimodal and high-resolution human brain
cerebellum lobule segmentation method. Unlike current tools that operate at
standard resolution ($1 \text{ mm}^{3}$) or using mono-modal data, the proposed
method improves cerebellum lobule segmentation through the use of a multimodal
and ultra-high resolution ($0.125 \text{ mm}^{3}$) training dataset. To develop
the method, first, a database of semi-automatically labelled cerebellum lobules
was created to train the proposed method with ultra-high resolution T1 and T2
MR images. Then, an ensemble of deep networks has been designed and developed,
allowing the proposed method to excel in the complex cerebellum lobule
segmentation task, improving precision while being memory efficient. Notably,
our approach deviates from the traditional U-Net model by exploring alternative
architectures. We have also integrated deep learning with classical machine
learning methods incorporating a priori knowledge from multi-atlas
segmentation, which improved precision and robustness. Finally, a new online
pipeline, named DeepCERES, has been developed to make available the proposed
method to the scientific community requiring as input only a single T1 MR image
at standard resolution.
