Segment Anything for Cell Tracking

• URL: http://arxiv.org/abs/2509.09943v1
• Date: Fri, 12 Sep 2025 03:19:35 GMT
• Title: Segment Anything for Cell Tracking
• Authors: Zhu Chen, Mert Edgü, Er Jin, Johannes Stegmaier,
• Abstract summary: We propose a zero-shot cell tracking framework for time-lapse microscopy images.<n>As a fully-unsupervised approach, our method does not depend on or inherit biases from any specific training dataset.<n>Our approach achieves competitive accuracy in both 2D and large-scale 3D time-lapse microscopy videos.
• Score: 2.0382881548515575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tracking cells and detecting mitotic events in time-lapse microscopy image sequences is a crucial task in biomedical research. However, it remains highly challenging due to dividing objects, low signal-tonoise ratios, indistinct boundaries, dense clusters, and the visually similar appearance of individual cells. Existing deep learning-based methods rely on manually labeled datasets for training, which is both costly and time-consuming. Moreover, their generalizability to unseen datasets remains limited due to the vast diversity of microscopy data. To overcome these limitations, we propose a zero-shot cell tracking framework by integrating Segment Anything 2 (SAM2), a large foundation model designed for general image and video segmentation, into the tracking pipeline. As a fully-unsupervised approach, our method does not depend on or inherit biases from any specific training dataset, allowing it to generalize across diverse microscopy datasets without finetuning. Our approach achieves competitive accuracy in both 2D and large-scale 3D time-lapse microscopy videos while eliminating the need for dataset-specific adaptation.

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