Continuous Representations in Bioimage Analysis: a Bridge from Pixels to the Real World
Virginie Uhlmann, EPFL STI LIB

Meeting • 12 December 2017

Abstract
Images and video sequences, either at the macro or microscopic level, are tools of choice to observe and characterize phenotypical variations. As a consequence, bioimage analysis has grown into an essentiel field of research. Recent advances in computer vision provide efficient tools which, given a set of examples, learn to predict which parts of the image hold relevant information. An obvious limitation of these methods is their discrete nature, leaving them bound to pixel grids and inherently unable to account for the fact that the real world is continuous. In this talk, we present a novel approach called landmark active contours which efficiently complement state-of-the-art pixel-based computer vision algorithms. While image acquisition turns real-world information into pixels, our method offers a way to go back from the digital to the continuous world. Landmark active contours consist in a mathematically well-defined continuous curve which uses information provided by pixel-based maps to automatically outline object in images. They simultaneously provide a segmentation algorithm and a particularly well-suited model for extracting precise quantitative information that characterize the objects. From their nature, landmark active contours are extremely flexible and can easily adapt to the wide variety of bioimages. We will describe their theoretical construction and show their use through several practical examples.