CE45 - Interfaces: mathématiques, sciences du numérique –biologie, santé

Interactive and Collaborative Learning for Vessel Segmentation – I-VESSEG

Submission summary

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.

Project coordination


The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.



Help of the ANR 232,667 euros
Beginning and duration of the scientific project: December 2022 - 42 Months

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