Patterning at the shoot apical meristem (SAM) is the emerging result of a complex interplay between genetically defined domains, differential tissue growth processes and highly dynamic hormonal signals, among which auxin, a phytohormone, is as a major player [1]. The progresses in live microscopy acquisition and the development of sensitive and quantitative biosensors [2] offer unprecedented tools to study the quantitative links between signaling input dynamics and their robust translation into organ development. However, to cope with the massive amount of images generated, advanced computational tools are required to make automatic quantification and data analysis possible [3, 4]. In this work, we addressed this issue be developing a pipeline that automatically performs quantification and spatio-temporal registration on time series of multi-channel confocal microscopy images with nuclei-located biosensors. It relies on image analysis methods to detect nuclei on a reference channel, quantify the other signals and perform sequence-wise image registration, but also on computational geometry to detect the epidermal cell layer and estimate curvature and surfacic growth. We then make use of the robust shape dynamics of the SAM to register all the time series into a common spatio-temporal referential. To do so, we propose an original model-based approach that relies on the optimization of a parametric 4D SAM shape model onto the detected nuclei point cloud, using an iterative energy minimization process already used on leaf images [5]. Aligning this model allows to project all the data points into a canonical cylindrical referential and to assign them a developmental time index, or in other words to insert them in to a developmental atlas. Applying this pipeline on r2DII auxin biosensor image sequences [2] and focusing on the epidermis field dynamics allows to assess quantitatively and in time the relation between auxin signaling and shape emergence in the SAM on large datasets, and provides a frame to study the joint dynamics of other markers and hormones in a commonly defined referential. References: [1] Vernoux T, Brunoud G, Farcot E, et al.; "The auxin signalling network translates dynamic input into robust patterning at the shoot apex", Molecular Systems B iology, 7, 508, 2011. [2] Liao C-Y, Smet W, Brunoud G, Yoshida S, Vernoux T, Weijers D; "Reporters for sensitive and quantitative measurement of auxin response", Nature Methods, Feb 2, 2015. [3] Barbier de Reuille P, Routier-Kierzkowska A-L, et al.; "MorphoGraphX: A platform for quantifying morphogenesis in 4D", eLife, 4:e05864, 2015. [4] Fernandez R, Das P, Mirabet V, Moscardi E, Traas J, Verdeil J-L, Malandain G, Godin C; "Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution", Nature Methods, 7, 547-553, 2010. [5] Cerutti G, Tougne L, Vacavant A, Coquin D; "A Parametric Active Polygon for Leaf Segmentation and Shape Estimation", Advances in Visual Computing, ISVC 2011, Lecture Notes in Computer Science, vol 6938, 2011.