Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images

2023/08/18 by Nikolas Ebert, Didier Stricker, Ebert, Nikolas +3
Biochemistry, Genetics and Molecular Biology · Engineering · Medicine · #Cell Image Analysis Techniques #Image Processing Techniques and Applications #COVID-19 diagnosis using AI

paper · pdf · doi:10.48550/arxiv.2308.09436

Abstract

Many medical or pharmaceutical processes have strict guidelines regarding continuous hygiene monitoring. This often involves the labor-intensive task of manually counting microorganisms in Petri dishes by trained personnel. Automation attempts often struggle due to major challenges: significant scaling differences, low separation, low contrast, etc. To address these challenges, we introduce AttnPAFPN, a high-resolution detection pipeline that leverages a novel transformer variation, the efficient-global self-attention mechanism. Our streamlined approach can be easily integrated in almost any multi-scale object detection pipeline. In a comprehensive evaluation on the publicly available AGAR dataset, we demonstrate the superior accuracy of our network over the current state-of-the-art. In order to demonstrate the task-independent performance of our approach, we perform further experiments on COCO and LIVECell datasets.

Related

• A Comparison of Bacterial Colonies Count from Petri Dishes Utilizing Hough Transform and Traditional Manual Counting
• AGAR a microbial colony dataset for deep learning detection
• Vision Transformers for Weakly-Supervised Microorganism Enumeration
• A Mask R-CNN approach to counting bacterial colony forming units in pharmaceutical development
• A Novel Framework for the Automated Characterization of Gram-Stained Blood Culture Slides Using a Large-Scale Vision Transformer