# Unsupervised cell segmentation by fast Gaussian Processes

arXiv: https://arxiv.org/abs/2505.18902 · HTML: https://mlsanity.com/paper/2505.18902/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets](https://mlsanity.com/paper/1406.7343/) (2014)
- [Local Gaussian process approximation for large computer experiments](https://mlsanity.com/paper/1303.0383/) (2013)
- [A multi-resolution approximation for massive spatial datasets](https://mlsanity.com/paper/1507.04789/) (2015)
- [Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data](https://mlsanity.com/paper/2011.10863/) (2020)
- [Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes](https://mlsanity.com/paper/2211.14692/) (2022)
- [Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification](https://mlsanity.com/paper/2305.08942/) (2023)
- [Calibrating an ice sheet model using high-dimensional binary spatial data](https://mlsanity.com/paper/1501.01937/) (2015)
- [The inverse Kalman filter](https://mlsanity.com/paper/2407.10089/) (2024)
- [Fast data inversion for high-dimensional Ornstein-Uhlenbeck processes from noisy measurements](https://mlsanity.com/paper/2501.01324/) (2025)

## Abstract (arXiv)

Cell boundary information is crucial for analyzing cell behaviors from time-lapse microscopy videos. 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 labeled 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. We derived robust thresholding criteria adaptive for heterogeneous images containing distinct brightness at different parts to separate objects from the background, and employed watershed segmentation to distinguish touching cell objects. Both simulated studies and real-data analysis of large microscopy images demonstrate the scalability and accuracy of our approach compared with the alternatives.