Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps

• URL: http://arxiv.org/abs/2308.04526v2
• Date: Thu, 11 Apr 2024 23:50:32 GMT
• Title: Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps
• Authors: Jordão Bragantini, Merlin Lange, Loïc Royer,
• Abstract summary: We describe a method for large-scale 3D cell-tracking through a segmentation selection approach. We show that this method achieves state-of-the-art results in 3D images from the cell tracking challenge. Our framework is flexible and supports segmentations from off-the-shelf cell segmentation models.
• Score: 1.015920567871904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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