Conference proceeding
Tracking Intermittent Particles with Self-Learned Visual Features
IEEE Xplore, Vol.2023-, pp.1-5
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
2023
DOI: 10.1109/ISBI53787.2023.10230664
Abstract
In time-lapse fluorescence imaging, single-particle-tracking is a powerful tool to monitor the dynamics of objects of interest, and extract information about biological processes. However, tracked particles can be subject to occlusion and intermittent detectability. When these phenomena persist over a few frames, tracking algorithms tend to produce multiple tracklets for the same particle. In this work, we introduce self-supervised learning of visual features to compare tracked particles, and we exploit both visual and positional distances to robustly stitch tracklets representing the same particle. We demonstrate the performance of our stitching framework on time-lapse fluorescence sequences of Hydra Vulgaris neurons. Results show high stitching precision, and reduction of errors made by previous algorithms on the same data by a factor of two.
Details
- Title: Subtitle
- Tracking Intermittent Particles with Self-Learned Visual Features
- Creators
- Raphael Reme - Analyse d'images biologiques - Biological Image AnalysisVictor Piriou - Analyse d'images biologiques - Biological Image AnalysisAlison Hanson - Columbia UniversityRafael Yuste - Columbia UniversityAlasdair Newson - Image, Modélisation, Analyse, GEométrie, SynthèseElsa Angelini - Image, Modélisation, Analyse, GEométrie, SynthèseJean-Christophe Olivo-Marin - Analyse d'images biologiques - Biological Image AnalysisThibault Lagache - Analyse d'images biologiques - Biological Image Analysis
- Resource Type
- Conference proceeding
- Publication Details
- IEEE Xplore, Vol.2023-, pp.1-5
- Series
- 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
- DOI
- 10.1109/ISBI53787.2023.10230664
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Number of pages
- 5
- Language
- English
- Date published
- 2023
- Academic Unit
- Psychiatry
- Record Identifier
- 9984822988902771
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