Microbial Cell-Adaptive Segmentation in Quantitative Phase Imaging

Quantitative phase imaging (QPI) enables label-free microscopy with exceptional signal-to-background contrast and provides direct measurements of key biophysical parameters such as cell mass. Despite these advantages, QPI lacks a generalized, accurate, and computationally efficient segmentation method, limiting its use in high-throughput single-cell biology. Here, we introduce microbial Cell-Adaptive Segmentation (mCAS), a purpose-built framework for QPI. mCAS is an object-based, modular pipeline that leverages the robust phase-edge signal to achieve accurate segmentation without Fourier filtering, deconvolution, or deep learning. It runs efficiently on standard hardware, is readily implemented in common software platforms, and requires no curated training data. mCAS consistently outperformed both intensity-based and deep learning approaches, achieving over 98% accuracy with up to tenfold faster processing. By eliminating the segmentation bottleneck, mCAS enables scalable, high-fidelity analysis of QPI datasets and provides a broadly applicable foundation for label-free, high-throughput single-cell biology. We validate its generality across diverse bacterial species, morphologies, and intracellular complexities.