ArticleNPJ systems biology and applications2025
Enhancing yeast cell tracking with a time-symmetric deep learning approach.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction.Scientific reports · 2026Article
- Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast.bioRxiv : the preprint server for biology · 2026Article
- Love-thy-neighbor: neural networks for tracking and lineage tracing in budding yeast.Bioinformatics advances · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based object tracking methods. In recent years, many applications have attempted to integrate deep-learning frameworks for this task, but most still heavily rely on consecutive frame-based tracking or other premises that hinder generalized learning. To address this issue, we aimed to develop a novel deep-learning-based tracking method that assumes cells can be tracked by their spatio-temporal neighborhood, without a restriction to consecutive frames. The proposed method has the additional benefit that the motion patterns of the cells can be learned by the predictor without any prior assumptions, and it has the potential to handle a large number of video frames with heavy artifacts. The efficacy of the proposed method is demonstrated through biologically motivated validation strategies and compared against multiple state-of-the-art cell tracking methods on budding yeast recordings and simulated samples.
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Registered trials
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