Biology subjects

Huijben, T. A. P. M.

Publications and source records attributed to Huijben, T. A. P. M..

3 recordsLinked to original sources

bioRxiv2024

inTRACKtive - A Web-Based Tool for Interactive Cell Tracking Visualization

We introduce inTRACKtive, an innovative web-based tool for interactive visualization and sharing of large 3D cell tracking datasets, eliminating the need for software installations or data downloads. Built with modern web technologies, inTRACKtive enables researchers to explore cell-tracking results from terabyte-scale microscopy data, conduct virtual fate-mapping experiments, and share these results via simple hyperlinks. The platform powers the Virtual Embryo Zoo, an online resource showcasing cell-tracking datasets from state-of-the-art light-sheet embryonic microscopy of six model organisms. inTRACKtives open-source code allows users to visualize their own data or host customized viewer instances. By providing easy access to complex tracking datasets, inTRACKtive offers a versatile, interactive, collaborative tool for developmental biology.

developmental biology↗

bioRxiv2024

Ultrack: pushing the limits of cell tracking across biological scales

Tracking live cells across 2D, 3D, and multi-channel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, achieving accurate cell tracking remains challenging, particularly in complex and crowded tissues where cell segmentation is often ambiguous. We present Ultrack, a versatile and scalable cell-tracking method that tackles this challenge by considering candidate segmentations derived from multiple algorithms and parameter sets. Ultrack employs temporal consistency to select optimal segments, ensuring robust performance even under segmentation uncertainty. We validate our method on diverse datasets, including terabyte-scale developmental time-lapses of zebrafish, fruit fly, and nematode embryos, as well as multi-color and label-free cellular imaging. We show that Ultrack achieves state-of-the-art performance on the Cell Tracking Challenge and demonstrates superior accuracy in tracking densely packed embryonic cells over extended periods. Moreover, we propose an approach to tracking validation via dual-channel sparse labeling that enables high-fidelity ground truth generation, pushing the boundaries of long-term cell tracking assessment. Our method is freely available as a Python package with Fiji and napari plugins and can be deployed in a high-performance computing environment, facilitating widespread adoption by the research community.

developmental biology↗

bioRxiv2021

Joint Registration of Multiple Point Clouds for Fast Particle Fusion in Localization Microscopy

We present a fast particle fusion method for particles imaged with single-molecule localization microscopy. The state-of-the-art approach based on all-to-all registration has proven to work well but its computational cost scales unfavourably with the number of particles N, namely as N2. Our method overcomes this problem and achieves a linear scaling of computational cost with N by making use of the Joint Registration of Multiple Point Clouds (JRMPC) method. Straightforward application of JRMPC fails as mostly locally optimal solutions are found. These usually contain several overlapping clusters, that each consist of well-aligned particles, but that have different poses. We solve this issue by repeated runs of JRMPC for different initial conditions, followed by a classification step to identify the clusters, and a connection step to link the different clusters obtained for different initializations. In this way a single well-aligned structure is obtained containing the majority of the particles. We achieve reconstructions of experimental DNA-origami datasets consisting of close to 400 particles within only 10 min on a CPU, with an image resolution of 3.2 nm. In addition, we show artifact-free reconstructions of symmetric structures without making any use of the symmetry. We also demonstrate that the method works well for poor data with a low density of labelling and for 3D data.

bioinformatics↗