Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets.

ABSTRACT: Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on task-specific imaging protocols including a task-specific set of imaging modalities. In this work, we

SUBMITTER: Dorent R 

PROVIDER: S-EPMC7116853 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature