Unsupervised cell segmentation by invariant information clustering

Wessel L. van Nierop; Jan-N. Schneider; Peter H. N. de With; Fons van der Sommen

doi:10.1117/12.3001442

2 April 2024 Unsupervised cell segmentation by invariant information clustering

Wessel L. van Nierop, Jan-N. Schneider, Peter H. N. de With, Fons van der Sommen

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129260V (2024) https://doi.org/10.1117/12.3001442
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wessel L. van Nierop, Jan-N. Schneider, Peter H. N. de With, and Fons van der Sommen "Unsupervised cell segmentation by invariant information clustering", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260V (2 April 2024); https://doi.org/10.1117/12.3001442

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