Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning

Abstract

We present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60 mm diameter agar-plate and analyzes these time-lapsed holograms using deep neural networks for rapid detection of bacterial growth and classification of the corresponding species. The performance of our system was demonstrated by rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples. These results were confirmed against gold-standard culture-based results, shortening the detection time of bacterial growth by >12 h as compared to the Environmental Protection Agency (EPA)-approved analytical methods. Our experiments further confirmed that this method successfully detects 90% of bacterial colonies within 7-10 h (and >95% within 12 h) with a precision of 99.2-100%, and correctly identifies their species in 7.6-12 h with 80% accuracy. Using pre-incubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L within 9 h of total test time. This computational bacteria detection and classification platform is highly cost-effective (~$0.6 per test) and high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing analytical methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time, also automating the identification of colonies, without labeling or the need for an expert.

arXiv comment24 pages, 6 figures

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• Densely Connected Convolutional Networks
• Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks

Cited by in corpus (14)

• On the use of deep learning for phase recovery
• Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
• Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
• All-optical complex field imaging using diffractive processors
• Ptychographic sensor for large-scale lensless microbial monitoring with high spatiotemporal resolution
• Generation of microbial colonies dataset with deep learning style transfer
• Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
• An Automated, Cost-Effective Optical System for Accelerated Anti-microbial Susceptibility Testing (AST) using Deep Learning
• Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor
• Self-Normalized Density Map (SNDM) for Counting Microbiological Objects
• Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast
• Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images
• Deep Learning-based Detection of Bacterial Swarm Motion Using a Single Image
• Wide-field Decodable Orthogonal Fingerprints of Single Nanoparticles Unlock Multiplexed Digital Assays