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

• Main Authors: Wang, Hongda, Koydemir, Hatice Ceylan, Qiu, Yunzhe, Bai, Bijie, Zhang, Yibo, Jin, Yiyin, Tok, Sabiha, Yilmaz, Enis Cagatay, Gumustekin, Esin, Rivenson, Yair, Ozcan, Aydogan
• Format: Journal Article
• Language: English
• Published: 29-01-2020
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Physics - Applied Physics; Physics - Instrumentation and Detectors
• DOI: 10.48550/arxiv.2001.10695

Light: Science & Applications (2020) 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.