An Artificial Intelligence Workflow for Defining Host-Pathogen Interactions

Fisch, Daniel; Yakimovich, Artur; Clough, Barbara; Wright, J. D.; Bunyan, Monique; Howell, Michael; Mercer, Jason; Frickel, Eva‐Maria

doi:10.1101/408450

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…The existing cell analytical platforms, such as CellProfiler and ImageJ, etc., have limited features for the analysis of phase contrast images [ 10 ] due to the complex image features and difficulty of detecting cell populations. In the last few years, deep learning methods such as CNNs have shown remarkable performances for a wide range of computer vision tasks.…”

Section: Introductionmentioning

confidence: 99%

AutoCellANLS: An Automated Analysis System for Mycobacteria-Infected Cells Based on Unstained Micrograph

et al. 2022

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The detection of Mycobacterium tuberculosis (Mtb) infection plays an important role in the control of tuberculosis (TB), one of the leading infectious diseases in the world. Recent advances in artificial intelligence-aided cellular image processing and analytical techniques have shown great promises in automated Mtb detection. However, current cell imaging protocols often involve costly and time-consuming fluorescence staining, which has become a major bottleneck for procedural automation. To solve this problem, we have developed a novel automated system (AutoCellANLS) for cell detection and the recognition of morphological features in the phase-contrast micrographs by using unsupervised machine learning (UML) approaches and deep convolutional neural networks (CNNs). The detection algorithm can adaptively and automatically detect single cells in the cell population by the improved level set segmentation model with the circular Hough transform (CHT). Besides, we have designed a Cell-net by using the transfer learning strategies (TLS) to classify the virulence-specific cellular morphological changes that would otherwise be indistinguishable to the naked eye. The novel system can simultaneously classify and segment microscopic images of the cell populations and achieve an average accuracy of 95.13% for cell detection, 95.94% for morphological classification, 94.87% for sensitivity, and 96.61% for specificity. AutoCellANLS is able to detect significant morphological differences between the infected and uninfected mammalian cells throughout the infection period (2hpi/12hpi/24hpi). Besides, it has overcome the drawback of manual intervention and increased the accuracy by more than 11% compared to our previous work, which used AI-aided imaging analysis to detect mycobacterial infection in macrophages. AutoCellANLS is also efficient and versatile when tailored to different cell lines datasets (RAW264.7 and THP-1 cell). This proof-of concept study provides a novel venue to investigate bacterial pathogenesis at a macroscopic level and offers great promise in the diagnosis of bacterial infections.

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Section: Introductionmentioning

confidence: 99%

AutoCellANLS: An Automated Analysis System for Mycobacteria-Infected Cells Based on Unstained Micrograph

et al. 2022

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“…14 Historically, the field of AI originates from an attempt to create fundamental and applied basics for machines with "intelligent properties". 15 ML, often considered an AI subfield, has significantly facilitated AI by providing a tangible toolset. Conversely, the traction some ML algorithms like artificial neural networks have gained in recent years may be to a larger extent attributed to the effort to make progress in AI.…”

Section: Introductionmentioning

confidence: 99%