The Image Processing and Pattern Recognition group contributes to the ISEVAC research project. This collaborative initiative, which brings together CREATIS (INSA Lyon), LaTIM (IMT-Atlantique), and our group, aims to develop clinically viable vascular segmentation tools. The project addresses key limitations of current methods, such as fragmented vessel trees and limited generalization, by focusing on three main challenges:
- Annotation quality and evaluation: Defining robust guidelines and metrics to assess and improve the quality of vascular annotations, in order to strengthen model training and validation across diverse imaging conditions.
- Topology-preserving segmentation: Reformulating segmentation as a recursive vessel tracking task, enabling more anatomically faithful reconstructions by explicitly following the branching structure of vascular networks.
- Semi-automatic methods guided by foundation models: Investigating interactive segmentation approaches where users provide rough centerlines, used as prompts for foundation models specialized in medical imaging to improve segmentation accuracy.
All tools will be integrated into a 3D Slicer plug-in, developed in collaboration with Kitware, to produce a user-friendly vascular segmentation tool suited for clinical and translational research.