In her research, Iris developed userfriendly tools for standardized and reproducible blood vessel analysis (Figure 1). These tools enabled the study of over 3,000 brain images, leading to the identification of several potential risk factors associated with aneurysms. She also used graph neural networks, a subfield of AI, to automatically detect and label important blood vessels and bifurcations (branching points). Unlike traditional methods, graph neural networks can capture complex relationships between vascular structures. These techniques can be used to recognize subtle patterns in data that could help identify new imaging markers for aneurysm development. One of the biggest obstacles in this field is the detection of very small blood vessels, some of which are less than 1 mm in diameter. Standard image analysis techniques often struggle to accurately extract these vessels, leading to incomplete data. To address this issue, Iris and her colleague Diewertje Alblas, developed a deep-learning based method that uses path optimization techniques guided by artery orientation. This approach successfully extracts even the smallest blood vessels, including those under 1 mm. 25 DAILY WACV Saturday Iris Vos Figure 1. Analysis of brain blood vessels. Upper: automated measurement of blood vessel diameter. Lower: automated measurement of bifurcation angle.
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