# Machine learning models for segmentation and classification of cyanobacterial cells

## TL;DR

This paper introduces Cypose, a new software using machine learning to accurately segment and classify cyanobacterial cells in microscopy images.

## Contribution

The paper presents the first machine learning models specifically designed for segmentation and classification of cyanobacterial cells.

## Key findings

- The segmentation models outperformed traditional methods and handled varied morphologies and imaging artifacts.
- The classification model successfully identified different cellular phenotypes using only image data.
- The models enable high-throughput analysis of dense cyanobacterial colonies and filaments.

## Abstract

Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

The online version contains supplementary material available at 10.1007/s11120-025-01140-x.