Work

Attention-driven UNet enhancement for accurate segmentation of bacterial spore outgrowth in microscopy images

Year: 2025

Type: article

Abstract: Analyzing microscopy images of large growing cell samples using traditional methods is a complex and time-consuming process. In this work, we have developed an attention-driven UNet-enhanced model using deep learning techniques to efficiently quantify the position, area, and circularity of bacterial spores and vegetative cells from images containing more than 10,000 bacterial cells. Our attention-driven UNet algorithm has an accuracy of 96%, precision of 82%, sensitivity of 81%, and specificity of 98%. Therefore, it can segment cells at a level comparable to manual annotation. We demonstrate the efficacy of this model by applying it to a live-dead decontamination assay. The model is provided in three formats: Python code, a Binder that operates within a web browser without needing installation, and a Flask Web application for local use.

Source: Scientific Reports

DOI: 10.1038/s41598-025-05900-6

Authors: Saqib Qamar, Dmitry Malyshev, Rasmus Öberg, Daniel P. G. Nilsson, Magnus Andersson

Institutions: Sohar University, Umeå University, Umeå Centre for Microbial Research

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: Vetenskapsrådet, Kempestiftelserna, Norges Miljø- og Biovitenskapelige Universitet