Timelines are common visualizations to represent and manipulate temporal data. However, timeline visualizations rarely consider spatio-temporal 3D data (e.g. mesh or volumetric models) directly. In this paper, leveraging the increased workspace and 3D interaction capabilities of virtual reality (VR), we first propose a timeline design space for 3D temporal data extending the timeline design space proposed by Brehmer et al. [7]. The proposed design space adapts the scale, layout and representation dimensions to account for the depth dimension and how the 3D temporal data can be partitioned and structured. Moreover, an additional dimension is introduced, the support, which further characterizes the 3D dimension of the visualization. The design space is complemented by discussing the interaction methods required for the efficient visualization of 3D timelines in VR. Secondly, we evaluate the benefits of 3D timelines through a formal evaluation (n=21). Taken together, our results showed that time-related tasks can be achieved more comfortably using timelines, and more efficiently for specific tasks requiring the analysis of the surrounding temporal context. Finally, we illustrate the use of 3D timelines with a use-case on morphogenetic analysis in which domain experts in cell imaging were involved in the design and evaluation process.

We introduce a new application for annotating images, with the purpose of constituting training datasets for machine learning algorithms. Our open-source software is meant to be easily used and deployed, configured to meet the annotation needs of any use case, and embeddable in crowdsourcing campaigns using the Amazon Mechanical Turk service.

Interactive segmentation consists in building a pixel-wise partition of an image, into foreground and background regions, with the help of user inputs. Most state-of-the-art algorithms use scribble-based interactions to build foreground and background models, and very few of these work focus on the usability of the scribbling interaction. In this paper we study the outlining interaction, which is very intuitive to non-expert users on touch devices. We present an algorithm, built upon the existing GrabCut algorithm, which infers both foreground and background models out of a single outline. We conducted a user study on 20 participants to demonstrate the usability of this interaction, and its performance for the task of interactive segmentation.

We propose to directly process 3D + t image sequences with mathematical morphology operators using a new classification of the 3D+t structuring elements. Several methods (filtering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebrafish embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebrafish early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it benefits from the intrinsic redundancy of the temporal dimension and minimizes the needs for human intervention in semi-automatic algorithms.

Investigating cell dynamics during early zebrafish embryogenesis requires specific image acquisition and analysis strategies. Multiharmonic microscopy, i.e., second- and third-harmonic generations, allows imaging cell divisions and cell membranes in unstained zebrafish embryos from 1- to 1000-cell stage. This paper presents the design and implementation of a dedicated image processing pipeline (tracking and segmentation) for the reconstruction of cell dynamics during these developmental stages. This methodology allows the reconstruction of the cell lineage tree including division timings, spatial coordinates, and cell shape until the 1000-cell stage with minute temporal accuracy and micrometer spatial resolution. Data analysis of the digital embryos provides an extensive quantitative description of early zebrafish embryogenesis.

We propose an extension of the classical morphological filtering based on openings by line segment structuring elements. It consists in filtering a 3D+time image with the opening by all the possible rotations of a segment in the 4D space, given an initial segment and a rotation angle. The method has been applied to remove noise from confocal laser scanning microscopy images of live zebrafish embryos engineered to fluorescently label all their cell membranes.