@conference {70301, title = {An investigation of unsupervised cell tracking and interactive fine-tuning}, booktitle = {2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year = {2025}, month = {2026 Feb 23}, abstract = {

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 mispredicted 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.

}, keywords = {Active learning, Annotations, Cell Tracking, Computer vision, Conferences, Data models, Fine-Tuning, Life sciences, Manuals, Microscopy, Supervised learning, Training, Unsupervised Deep Learning}, doi = {10.1109/ICCVW69036.2025.00610}, url = {https://doi.org/10.1109/ICCVW69036.2025.00610}, author = {Lalit, Manan and Funke, Jan} } @conference {69206, title = {Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images}, booktitle = {2023 IEEE/CVF International Conference on Computer Vision (ICCV)}, year = {2023}, month = {2023 Oct 01}, publisher = {IEEE}, organization = {IEEE}, address = {Paris, France}, abstract = {

Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the spatial offsets between patches cropped from the same object are preserved. Those learnt embeddings can be used to delineate individual objects and thus obtain instance segmentations. Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches. Together, this forms an unsupervised cell instance segmentation method which we evaluate on nine diverse large-scale microscopy datasets. Segmentations obtained with our method lead to substantially improved results, compared to state-of-the-art baselines on six out of nine datasets, and perform on par on the remaining three datasets. If ground-truth annotations are available, our method serves as an excellent starting point for supervised training, reducing the required amount of ground-truth needed by one order of magnitude, thus substantially increasing the practical applicability of our method. Source code is available at github.com/funkelab/cellulus.

}, doi = {10.1109/ICCV51070.2023.01944}, url = {https://ieeexplore.ieee.org/document/10377649/}, author = {Wolf, Steffen and Lalit, Manan and McDole, Katie and Funke, Jan} }