Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Attention-driven UNet enhancement for accurate segmentation of bacterial spore outgrowth in microscopy images
Umeå University, Faculty of Science and Technology, Department of Physics. Faculty of Computing and IT, Sohar University, Sohar, Oman.
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-0496-6692
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-0168-0197
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-1303-0327
Show others and affiliations
2025 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 15, no 1, article id 20177Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Nature Portfolio , 2025. Vol. 15, no 1, article id 20177
Keywords [en]
Contamination, Deep learning, Spores
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-241719DOI: 10.1038/s41598-025-05900-6ISI: 001512788100022PubMedID: 40542045Scopus ID: 2-s2.0-105008715941OAI: oai:DiVA.org:umu-241719DiVA, id: diva2:1981451
Funder
The Kempe Foundations, JCK-2129.3Available from: 2025-07-04 Created: 2025-07-04 Last updated: 2025-09-30Bibliographically approved
In thesis
1. Spotlight the killer: detecting harmful chemical and biological agents using optical spectroscopy
Open this publication in new window or tab >>Spotlight the killer: detecting harmful chemical and biological agents using optical spectroscopy
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Lyset på mördaren : detektion av skadliga kemiska och biologiska ämnen med hjälp av optisk spektroskopi
Abstract [en]

Harmful chemical and biological agents are a significant threat to health and prosperity worldwide. Recent years have seen an increase in wars and conflicts around the globe, raising concerns about the potential deployment of chemical and biological warfare agents. On a less speculative level, harmful chemicals such as narcotic substances cause immense humanitarian and economic damage through overdoses and associated healthcare costs, while microbes such as pathogenic bacteria and parasites cause hospital-acquired infections and food spoilage at a cost of approximately 1 trillion euros every year. To combat the threat of these harmful agents, we must thus develop rapid and effective detection and diagnostic methods for harmful agents, allowing us to effectively deploy specific treatments and preventative measures.

Classically, while there exist numerous methods for the detection of both harmful chemical and biological agents, they often come with limitations that inhibit their effectiveness. These inhibitions often take the form of bulky equipment that is difficult to apply in the field or time-consuming preparation and measurement processes.

In this thesis we will explore an alternative category of assays for detecting and characterizing harmful materials – optical spectroscopy. Optical spectroscopy is a category of material characterization methods that use light to probe a material. While probing the material, we receive a signal characteristic of the molecules, chemical, and biological structure of our material. These optical spectroscopic methods, such as Raman spectroscopy and fluorescence spectroscopy, can be used to characterize a material within the span of minutes or even seconds, making them ideal for detection applications. Furthermore, they can often be made portable or even handheld, making them a great tool for initial field indication of harmful materials, ahead of thorough lab analysis.

I sincerely hope the studies presented herein can serve as a stepping stone to future technologies and detection assays, capable of saving both money and lives. 

Place, publisher, year, edition, pages
Umeå: Umeå University, 2025. p. 72
Keywords
Sensing, Raman spectroscopy, SERS, Fluorescence spectroscopy, CWA, nerve agents, bacterial spores, Cryptosporidium
National Category
Atom and Molecular Physics and Optics
Identifiers
urn:nbn:se:umu:diva-244830 (URN)978-91-8070-780-0 (ISBN)978-91-8070-779-4 (ISBN)
Public defence
2025-10-24, Aula Anatomica, Biologihuset, 907 36, Umeå, Umeå, 13:00 (English)
Opponent
Supervisors
Note

This work was done in collaboration with, and with support from, the Swedish Defece Research Agency (FOI).

Available from: 2025-10-03 Created: 2025-09-30 Last updated: 2025-10-22Bibliographically approved

Open Access in DiVA

fulltext(4876 kB)64 downloads
File information
File name FULLTEXT01.pdfFile size 4876 kBChecksum SHA-512
cd1dbb071d0f5307d2d4730391e98e1c2a8dc2ea38899b5e0b54623f4851490d31aa6f45d2a651fc9e9cef3ba2d54040d3ef4fbeb393d95fc2a7b6089da99b49
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Qamar, SaqibMalyshev, DmitryÖberg, RasmusNilsson, DanielAndersson, Magnus

Search in DiVA

By author/editor
Qamar, SaqibMalyshev, DmitryÖberg, RasmusNilsson, DanielAndersson, Magnus
By organisation
Department of PhysicsUmeå Centre for Microbial Research (UCMR)
In the same journal
Scientific Reports
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 64 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 419 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf