Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging

Ferrari, Alessandro; Lombardi, Stefano; Signoroni, Alberto

doi:10.1016/j.patcog.2016.07.016

Cited by 132 publications

(90 citation statements)

References 34 publications

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“…The recent introduction of a digital counting pen and a variety of downloadable phone applications may revolutionize colony counting, as low cost and free automated options are available. On the other end of the spectrum, there are digital microbiology systems which are designed to fully automate high throughput clinical microbiology labs, which take high quality images of plates during the incubation process, visualize them, store and analyse the data (Ferrari et al 2017). Such cutting edge systems are not applicable to small, low budget operations and were not considered in this study.…”

Section: Introductionmentioning

confidence: 99%

In search of a counter you can count on: relative efficacy of human visual and automated colony counting

Young

Rieman

Walden

et al. 2018

Lett Appl Microbiol

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Colony quantification is essential in clinical and research settings as well as pedagogy at the college level. Human visual (HV) counting, the most common method, is time consuming and fraught with errors. The time, accuracy and precision of HV counting were compared to a high-end professional automated counter, an inexpensive phone application and a free phone application. Low cost benefits of increased speed and accuracy with automated counting are maximized when counting single round colonies; but much reduced if colonies have irregular morphology or demonstrate haemolysis.

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Section: Introductionmentioning

confidence: 99%

In search of a counter you can count on: relative efficacy of human visual and automated colony counting

Young

Rieman

Walden

et al. 2018

Lett Appl Microbiol

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“…in tumor detection 44 . In particular, advances in image recognition enable the automation of so far manual tasks, such as the possibility to automate the counting of cell colonies grown on petri-dishes, using digital imaging 45 . Surprisingly, the practical implementation of these hybrid approaches combining machine learning and the extended knowledge available about biological systems is still in its infancy in bioindustries.…”

Section: Modeling With Hybrid Approaches: Combine To Effectively Implmentioning

confidence: 99%

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

et al. 2020

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In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.npj Systems Biology and Applications (2020) 6:6 ; https://doi.

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“…Recently, with the quick development of artificial intelligence technology, the deep learning model has shown excellent performances in solving a wide range of problems in scientific and industrial fields (Ferrari, Lombardi and Signoroni ; Sze et al . ; Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

A high accurate automated first‐break picking method for seismic records from high‐density acquisition in areas with a complex surface

Chao

Xuefeng

et al. 2020

Geophysical Prospecting

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A B S T R A C TAs the application of high-density high-efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first-break picking work of massive low signal-tonoise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti-noise to low signal-to-noise ratio primary wave, this paper proposes a high accurate automated first-break picking method for low signal-to-noise ratio primary wave from high-density acquisition in areas with a complex surface. Firstly, this method determines first-break time window using multi-azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi-angle comprehensive quality evaluation to first breaks according to the following sequence: 'single trace → spread → single shot → multiple shots' to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi-azimuth time window spatial interpolation technology provides the base for accurately picking first-break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal-to-noise ratio primary waves; the multi-angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal-to-noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first-break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal-to-noise ratio seismic data, has a significant effect on seismic records from high-density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near-surface modelling and statics calculation.

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Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging

Cited by 132 publications

References 34 publications

In search of a counter you can count on: relative efficacy of human visual and automated colony counting

In search of a counter you can count on: relative efficacy of human visual and automated colony counting

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

A high accurate automated first‐break picking method for seismic records from high‐density acquisition in areas with a complex surface

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