Asea Brown Boveri Ltd. (ABB) Award 2020 – Virginie Uhlmann

For her unifying formulation of active contour models and the development of novel, user-friendly computational tools for bioimage analysis.

The problem of identifying the outline of objects in images can be approached from two starting points, either by considering localized features or by searching for global contours. Features are regions or points of interest and usually include a description of the local properties of the image around them. The definition of a feature is flexible. Most often, it consists of a list of desirable properties inspired by the application at hand. Contours, on the other hand, are (portions of) curves that can be delineated using deformable models. Splines are in particular at the core of a large family of such models called spline-based active contours.

These methods can be customized and adapted to outline a large variety of objects in many types of images. In this thesis, we unify these two strategies by bridging automated feature detection and spline-based active contour segmentation for bioimage analysis. We propose a semiautomated segmentation algorithm relying on Hermite interpolation, which evolves a curve in the image to outline objects of interest using information provided by steerable feature detectors.

The approach is generic enough to be used in a wide variety of data, as illustrated through collaborative work on real bioimage analysis problems.