An Investigation of Unsupervised Cell Tracking and Interactive Fine-Tuning

Manan Lalit, Jan Funke; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 5851-5859

Most existing deep learning-based cell tracking methods rely on supervised learning, requiring large-scale annotated datasets that are often unavailable in real-world scenarios. Moreover, many approaches lack tools and methods for correcting mis-predicted links or incorporating corrections through fine-tuning. These limitations contribute to the limited adoption of deep learning-based tracking methods in the life sciences, where manual tracking remains the predominant approach. To reduce the annotation burden and enable model training without extensive labeled data, we introduce a loss function for unsupervised training. Our method leverages the predictable dynamics inherent in many biological processes, providing an initialization that does not require an annotated dataset. We further investigate how minimal user-provided annotations can refine tracking accuracy. To this end, we propose an active learning framework that selectively identifies uncertain decisions within the tracking graph, allowing for efficient annotation of the most informative data points. We evaluate our approach on two microscopy datasets, demonstrating the effectiveness of both our unsupervised training strategy and active learning scheme in improving tracking performance. Our implementation and reproducible experiments are available at github.com/funkelab/attrackt and github.com/funkelab/attrackt_experiments, respectively.