Unsupervised Cell Segmentation by Invariant Information Clustering

Cell segmentation is a crucial task in brightfield microscopy for cellular analysis. Conventional segmentation methods, including thresholding and supervised deep learning approaches, have limitations due to weak cell boundaries and the requirement for expert annotations of cells. To address these challenges, this work proposes an unsupervised cell-segmentation model. Specifically, the model maximizes mutual information between an artificially created data pair to train a neural network for cell segmentation. The proposed method achieves the fastest inference time and requires only a small fraction of network parameters compared to reported state-of-the-art alternatives from literature, making the proposal more efficient for live-cell experiments where large amounts of data are collected. A quantitative comparison with other state-of-the-art models shows that the unsupervised model outperforms relevant supervised models in terms of the Jaccard index. In detail, the unsupervised model achieves a remarkable Jaccard score of 0.71 on a out-of-sample proprietary dataset which is fully annotated. This represents a notable improvement of 4.0% compared to the supervised Cellpose model and an impressive 11.4% increase compared to the unsupervised Leopart model. In conclusion, the proposed model effectively segments cells of six types without the need for expert annotations, thereby making it a valuable tool for cellular analysis. The source code is available at https://github.com/wesselvannierop/wescell.