Cerebellum Lobule Segmentation

Cerebellar lobule segmentation aims to automatically delineate the cerebellum's intricate subregions from brain imaging data, primarily MRI scans. Current research focuses on improving segmentation accuracy using advanced deep learning architectures, often incorporating multimodal data (e.g., T1 and T2 weighted images) and ultra-high resolution scans to overcome the challenges posed by the cerebellum's complex folded structure. These advancements are crucial for studying cerebellar involvement in neurological diseases and surgical outcomes, enabling more precise quantification of damage and improved understanding of structure-function relationships. Furthermore, efforts are underway to develop robust and reliable methods that require minimal manual intervention, leveraging techniques like unsupervised domain adaptation and Bayesian algorithms.