Interactive and Collaborative Learning for Vessel Segmentation – I-VESSEG

I-VESSEG aims to close the gap hindering the use of 3D vessel segmentation tools to assist clinicians in angiographic clinical routines. The project will build on learning-based techniques and will address their limitations regarding the need for large, fully annotated training sets and their poor generalization. I-VESSEG will use interactive learning to allow continual training from weak annotations provided by the user, as data becomes available. To facilitate data access for training, I-VESSEG will be formulated in a collaborative federated learning paradigm that enables learning without the need for sensitive data sharing or centralized storage. Finally, by relying on domain adaptation and generalization techniques, I-VESSEG will be applicable in a transparent manner to any cerebrovascular imaging modality. Through a unique collaboration with a network of international excellence partners in neuroimaging, the translational value of this project will be demonstrated on two use cases of primary societal impact: 1) the diagnosis of multiple sclerosis; and 2) the detection of intracranial stenosis, a risk factor for stroke.