Daniela Pérez Guerrero, Jesús Manuel Antúnez Domínguez, Aurélie Vigne, Daniel Midtvedt, Wylie Ahmed, Lisa Muiznieks, Giovanni Volpe and Caroline Beck Adiels.
SPIE-ETAI, San Diego, CA, USA, 03 – 07 August 2025
Date: 6 August 2025
Time: 2:30 PM – 2:45 PM PDT
Place: Conv. Ctr. Room 4
Biofilms are structured communities of microorganisms that play a crucial role in medicine, biotechnology, and ecology, contributing to microbial adaptation to any environment. Despite their significance, understanding their formation, development, and behavior remains a challenge for the community. We utilize high-throughput droplet microfluidics to enable biofilm growth in miniaturized environments, generating extensive time-lapse bright-field microscopy images. To overcome experimental constraints, including dense structural heterogeneity and skewed illumination, we developed a deep learning-based segmentation approach capable of identifying biofilm structures in complex imaging conditions. Our method operates in an unsupervised manner, reducing the need for ground truth annotations and mitigating the introduced bias of manual segmentation approaches.
Our unsupervised model effectively detects and quantifies biofilm structures, even in late-stage growth, where traditional segmentation techniques fail. The neural network demonstrates robust performance across the development cycle, distinguishing biofilm boundaries and bacteria aggregates separated from the main biofilm structure despite imaging inconsistencies. Additionally, our approach reduces manual intervention, streamlining the analysis of high-throughput biofilm imaging data.
This AI-powered segmentation technique provides a reliable and scalable tool for biofilm analysis, addressing key limitations of conventional methods. By bridging the gap between microbiology research and automated image analysis, our approach facilitates more efficient and reproducible biofilm studies.
