Unsupervised cell segmentation by fast Gaussian Processes

Abstract Excerpt: Existing supervised cell segmentation tools, such as ImageJ, require tuning various parameters and rely on restrictive assumptions about the shape of the objects. While recent supervised segmentation tools based on convolutional neural networks enhance accuracy, they depend on high-quality labelled images, making them unsuitable for segmenting new types of objects not in the database. We developed a novel unsupervised cell segmentation algorithm based on fast Gaussian processes for noisy microscopy images without the need for parameter tuning or restrictive assumptions about the shape of the object.

Recommended citation: Baracaldo, L., King, B., Yan, H., Lin, Y., Miolane, N., & Gu, M. (2025). Unsupervised cell segmentation by fast Gaussian Processes. arXiv preprint arXiv:2505.18902.

The first three authors contributed equally.