Speaker
Description
Cell tracking and lineage provide unique insights to study bacterial growth and dynamics. Tracking strongly relies on segmentation quality, and integrating accurate and robust segmentation algorithms is a key challenge when developing end-to-end tracking tools. Omnipose, a state of the art deep learning algorithm developed for bacteria segmentation, proved to outperform more traditional segmentation approaches. Here we introduce TrackMate-Omnipose, a user-friendly tracking interface, leveraging Omnipose for bacterial tracking and lineaging, for the Fiji ecosystem.
We provide a general methodology to use TrackMate-Omnipose and present several applications exemplifying a wide range of use cases to illustrate the usability, performance and robustness of the tool. In particular, we rely on automatic parameter estimation to choose the best algorithms and their optimal parameters for a specific application. We also introduce tools to conveniently annotate segmentation and tracking results within TrackMate, and use them to train custom DL models for segmentation and generate tracking ground truth.
| Authors | Marie Anselmet*, Laura Xénard, Gaëlle Letort, Marvin Albert, Minh-Son Phan, Stéphane Rigaud, Rodrigo Arias-Cartín, Samia Hicham, Laura Pokorny, Frédéric Barras, Ivo Gomperts Boneca, Giulia Manina, Jean-Yves Tinevez |
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| Keywords | Cell tracking, segmentation, deep-learning, TrackMate, Omnipose, Fiji, bacteria, bacterial growth, bacterial dynamics, cell lineage, user-friendly interface |
