Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929311651463168 |
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| author | Bragantini, Jordão Lange, Merlin Royer, Loïc |
| author_facet | Bragantini, Jordão Lange, Merlin Royer, Loïc |
| contents | In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii) It achieves competitive results with or without deep learning, which requires 3D annotated data, that is scarce in the fluorescence microscopy field. The proposed method computes cell tracks and segments using a hierarchy of segmentation hypotheses and selects disjoint segments by maximizing the overlap between adjacent frames. We show that this method achieves state-of-the-art results in 3D images from the cell tracking challenge and has a faster integer linear programming formulation. Moreover, our framework is flexible and supports segmentations from off-the-shelf cell segmentation models and can combine them into an ensemble that improves tracking. The code is available https://github.com/royerlab/ultrack. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_04526 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps Bragantini, Jordão Lange, Merlin Royer, Loïc Computer Vision and Pattern Recognition In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii) It achieves competitive results with or without deep learning, which requires 3D annotated data, that is scarce in the fluorescence microscopy field. The proposed method computes cell tracks and segments using a hierarchy of segmentation hypotheses and selects disjoint segments by maximizing the overlap between adjacent frames. We show that this method achieves state-of-the-art results in 3D images from the cell tracking challenge and has a faster integer linear programming formulation. Moreover, our framework is flexible and supports segmentations from off-the-shelf cell segmentation models and can combine them into an ensemble that improves tracking. The code is available https://github.com/royerlab/ultrack. |
| title | Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.04526 |