MDMR: Balancing Diversity and Redundancy for Annotation-Efficient Fine-Tuning of Pretrained Cell Segmentation Models

Pretrained cell segmentation models have simplified and accelerated microscopy image analysis, but they often perform poorly on challenging datasets. Although these models can be adapted to new datasets with only a few annotated images, the effectiveness of fine-tuning depends critically on which images are selected for annotation. To address this, we propose MDMR (Maximum Diversity Minimum Redundancy), a novel algorithm that selects the most informative subset of images by explicitly balancing diversity and redundancy in feature space. We evaluate MDMR under an extremely low annotation budget of just two images per dataset for fine-tuning the pretrained Cellpose Cyto2 model on four different 2D datasets from the Cell Tracking Challenge. MDMR consistently outperforms six competitive active learning and subset selection methods and approaches the performance of fully-supervised finetuning. The results show that explicitly balancing diversity and redundancy enables stable and annotation-efficient adaptation of pretrained cell segmentation models. Code is publicly available at: https://github.com/eiram-mahera/mdmr .