Machine Learning Models for Segmentation and Classification of Cyanobacterial Cells

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.

Plant phenotyping relevance

シアノバクテリア細胞の画像セグメンテーションと細胞表現型分類を行うソフトウェアおよび機械学習手法の開発が中心であり、植物細胞の形態・生存状態を抽出するフェノタイピング手法に該当する。

Code and data availability

The paper's segmentation/classification models and analysis code are publicly available in the authors' GitHub repository (cameronlab/cypose). The microscopy training datasets are not public and are available only upon request.