Unsupervised Cell Segmentation by Fast Gaussian Processes

We present an unsupervised cell segmentation method based on fast Gaussian processes that accurately handles noisy, heterogeneous microscopy images without parameter tuning, shape assumptions, or labeled data, and outperforms existing approaches in both simulated and large-scale real datasets.