Robust Cell Segmentation for Size Distribution Estimation via Synthetic-Data Training

Abstract

Polyhydroxyalkanoates (PHAs) are biodegradable and biocompatible plastics, yet large-scale production remains limited by costly batch operations and the lack of online analytical tools. Monitoring cell size distribution serves as a strong surrogate for intracellular PHA content, but training robust cell segmentation models for industrial bioprocesses requires extensive manual annotation, which is highly labor-intensive and impractical for densely populated microscopy images. To address this critical bottleneck, an annotation-free cell segmentation and size estimation pipeline tailored for real time monitoring is presented. Rather than relying on architectural modifications, this framework introduces a system-level automated training strategy: individual cells are automatically extracted from diluted-sample microscopy images using edge enhancement and rule-based binarization, then augmented and composited onto heterogeneous backgrounds to emulate the texture of undiluted, dense cultures. This fully synthetic data generation eliminates the need for manual labeling while enabling robust training of instance segmentation models. When implemented using a Mask R-CNN backbone, the proposed pipeline consistently tracks flow cytometry forward-scatter (FSC) distribution trends under diverse imaging conditions and achieves higher correlation with FSC data compared to Cellpose and CellSAM, representative foundation models for cell segmentation. This annotation-free methodology provides a practical and reliable automated online monitoring solution for intelligent PHA manufacturing.