Unsupervised cell segmentation by fast Gaussian Processes
Journal:
arXiv
Published Date:
May 24, 2025
Abstract
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 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. 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.
