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- Le Blanc, Laure
- 作成者
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- Rigaud, Stéphane
- 作成者
メタデータ
- 公開日
- 2022-02-15
- DOI
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- 10.5281/zenodo.6087728
- 10.5281/zenodo.6087729
- 10.5281/zenodo.6255991
- 公開者
- Zenodo
- データ作成者 (e-Rad)
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- Pylvänäinen, Joanna W.
- Tinevez, Jean-Yves
- Jacquemet, Guillaume
- Le Blanc, Laure
- Rigaud, Stéphane
説明
TrackMate now offers multiple detections and linking algorithms. Each of these algorithms needs to be configured with a parameter set that can take a wide range of values. While adequate values for these parameters can often be estimated intuitively, a systematic approach is often desirable. Indeed optimizing tracking parameters through visual assessment can be difficult when following many objects. To this end, we developed a new tool called TrackMate-Helper, which performs automatic parameter sweeps over any combination of detector and tracker available in TrackMate and measures the tracking results accuracy using the Cell-Tracking-Challenge (CTC) metrics. TrackMate-Helper requires an input image and the corresponding tracking ground-truth. Importantly, TrackMate-Helper is built as an end-user tool with a user-friendly interface that conveniently allows configuring parameter sweeps over many combinations of tracking parameters. We envision that this systematic approach will benefit medium and high-throughput automatic tracking studies. By optimizing the tracking parameters on one movie, users will be able to find optimal tracking parameters for the rest of their dataset. Here, we used TrackMate-Helper to assess the performance of TrackMate on four datasets that cover a wide range of biological and imaging situations: Migrating cancer cells imaged using fluorescence microscopy to visualize their nuclei. In this dataset, the cells are densely packed and divided during the experiment . Migrating T-cells imaged using phase-contrast microscopy). Neisseria meningitidis bacterial growth on agar pads. The bacteria are fluorescently labeled to visualize their membrane. Starting from a few single bacteria, the cells quickly divide several times and swarm the field of view. Glioblastoma-astrocytoma cells imaged using bright-field microscopy at high resolution. The table included reports the best results for each detector and tracker combination. We tested from 3000 to 20000 different parameter settings for each combination.