Deep neural networks have become the go-to method for biomedical instance segmentation. Generalist models like Cellpose demonstrate state-of-the-art performance across diverse cellular data, though their effectiveness often degrades on domains that differ from their training data. While supervised fine-tuning can address this limitation, it requires annotated data that may not be readily available. We propose SelfAdapt, a method that enables the adaptation of pre-trained cell segmentation models without the need for labels. Our approach builds upon student-teacher augmentation consistency training, introducing L2-SP regularization and label-free stopping criteria. We evaluate our method on the LiveCell and TissueNet datasets, demonstrating relative improvements in AP0.5 of up to 29.64% over baseline Cellpose. Additionally, we show that our unsupervised adaptation can further improve models that were previously fine-tuned with supervision. We release SelfAdapt as an easy-to-use extension of the Cellpose framework. The code for our method is publicly available at https: //github.com/Kainmueller-Lab/self_adapt.
@article{arxiv.2508.11411,
title = {SelfAdapt: Unsupervised Domain Adaptation of Cell Segmentation Models},
author = {Fabian H. Reith and Jannik Franzen and Dinesh R. Palli and J. Lorenz Rumberger and Dagmar Kainmueller},
journal= {arXiv preprint arXiv:2508.11411},
year = {2025}
}
Comments
8 pages, 3 figures. To appear in the proceedings of the BioImage Computing (BIC) Workshop @ ICCVW 2025. This is the accepted author manuscript (camera-ready version)