3D automated nuclear morphometric analysis using Active Meshes Alexandre Dufour 1,3 , JooHyun Lee 2 , Nicole Vincent 3 , Regis Grailhe 2 and Auguste Genovesio 1 1 Image Mining Group, Institut Pasteur Korea 2 Dynamic Imaging Platform, Institut Pasteur Korea 3 Intelligent Perception Systems (SIP-CRIP5) team, Paris Descartes University Correspondence: alexandre.dufour@ip-korea.org or agenoves@ip-korea.org Abstract. Recent advances in bioimaging have allowed to observe bio- logical phenomena in three dimensions in a precise and automated fash- ion. However, the analysis of depth-stacks acquired in fluorescence mi- croscopy constitutes a challenging task and motivates the development of robust methods. Automated computational schemes to process 3D multi-cell images from High Content Screening (HCS) experiments are part of the next generation methods for drug discovery. Working toward this goal, we propose a fully automated framework which allows fast seg- mentation and 3D morphometric analysis of cell nuclei. The method is based on deformable models called Active Meshes, featuring automated initialization, robustness to noise, real-time 3D visualization of the ob- jects during their analysis and precise geometrical shape measurements thanks to a parametric representation of each object. The framework has been tested on a low throughput microscope (classically found in research facilities) and on a fully automated imaging platform (used in screening facilities). We also propose shape descriptors and evaluate their robustness and independence on fluorescent beads and on two cell lines. 1 Introduction & related efforts The combination of microscopy and robotics enables to perform 2D visual cell based experiments in parallel and in a fully automated fashion. As a consequence, the exponential increase of images to analyze has motivated the development of fully automated frameworks. However, 2-dimensionality has some limitations, in particular for objects that are heterogeneous along the depth axis such as cell nuclei. Much more information can be obtained by acquiring depth-stacks of images, which allows to analyze the entire 3D structure of cellular or sub-cellular compartments [1]. The cell nuclear morphology constitutes a good start for such a study. A large array of biological functions is accompanied by major changes in the geometry of the nucleus [2]. Determining exactly how geometric characteristics relate to cellular function requires accurate 3D morphological information. In addition to quantitative measurements, visual observation is also a key aspect of scene interpretation and understanding. Yet, visualizing a 3D scene