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Related Experiment Video

Updated: Jul 10, 2026

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

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Published on: June 21, 2022

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

Han Bit Kim¹, Chaeeun Lee¹, Naeun Lee²

¹Department of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea.

Biotechnology Journal

|July 9, 2026

Summary

This summary is machine-generated.

This study introduces an annotation-free method for cell segmentation, crucial for monitoring polyhydroxyalkanoates (PHAs) production. The automated pipeline enables real-time analysis, overcoming limitations of manual annotation in industrial bioprocesses.

Keywords:

PHA annotation‐free cell segmentation online monitoring synthetic data

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Biotechnology
Microscopy
Data Science

Background:

Polyhydroxyalkanoates (PHAs) are biodegradable polymers with potential for sustainable manufacturing.
Current PHA production relies on batch processes and lacks real-time monitoring tools.
Manual annotation of cell images for PHA content estimation is labor-intensive and impractical for dense cultures.

Purpose of the Study:

To develop an annotation-free cell segmentation and size estimation pipeline for real-time monitoring of PHA production.
To overcome the bottleneck of manual data labeling in industrial bioprocesses.
To enable intelligent manufacturing of PHAs through automated online monitoring.

Main Methods:

An automated training strategy using synthetic data generation.
Edge enhancement and rule-based binarization for cell extraction from diluted samples.
Augmentation and compositing of cells onto heterogeneous backgrounds to mimic dense cultures.
Instance segmentation model training (Mask R-CNN backbone) using generated synthetic data.

Main Results:

The pipeline successfully generates synthetic data, eliminating the need for manual labeling.
The annotation-free method enables robust training of instance segmentation models.
The system consistently tracks flow cytometry forward-scatter (FSC) distribution trends.
Achieved higher correlation with FSC data compared to Cellpose and CellSAM.

Conclusions:

The developed annotation-free methodology offers a practical and reliable solution for automated online monitoring in PHA manufacturing.
This approach facilitates intelligent PHA production by providing real-time process insights.
Reduces the labor and cost associated with manual image annotation for bioprocess monitoring.