A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter

Kitahara, Yukina; Itani, Ayaka; Oda, Yosuke; Okamura, Makoto; Mizoshiri, Mizue; Shida, Yosuke; Nakamura, Toru; Kasahara, Keiji; Ogasawara, Wataru

doi:10.1007/s00253-022-12002-0

Cited by 4 publications

(2 citation statements)

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“…An application of ISM based on an image analysis algorithm that uses detection of regional maxima can be used to determine the average size of single cells enabling on-line size monitoring [6]. With the progress in image processing technology, it turns out to be easier to extract information on several parameters, enabling the use of this technology for the measurement of microbial morphology, in fact image analysis and automated methods can be used to provide a better and detailed understanding of the morphology of Saccharomyces cerevisiae cells; in this context are well suited image-processing techniques to monitor yeasts cultivation directly using high-speed cameras [7], [8] and machine learning approaches [9] using classical segmentation algorithms [10], [11] and ones based on a set of relevant individual cell features based on first and second order histograms and waveletbased texture measurement extracted from the microscope images of the yeast cells to represent the morphological characteristics in a more sophisticated way [12]. Counting procedures can be computed using the traditional microscopy or more effectively resorting to automatic systems as the image processing techniques and the segmentation algorithms cited before or bright-field microscopy and dye-exclusion methods [13].…”

Section: Related Results In the Literaturementioning

confidence: 99%

Use of Artificial Intelligence in optical microscope imaging

Carratù,

Gallo,

Laino

et al. 2023

Proceedings of the 19th IMEKO TC10 International Conference on Measurement for Diagnostics, Optimization and Control on Measure

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Identification and morphological analysis of microorganisms are of high interest in scientific research, especially in the medical field and food industry. Identification allows rapid functional characterization based on similarities with known related species enabling to confirm the identity of an isolate used, for example, in a trademarked industrial process. Monitoring of microorganisms within a given ecosystem and analysis of the morphological characteristics of the observed species enable quality control of the process under analysis. Such procedures are carried out manually in specialized laboratories by trained personnel using the appropriate optical equipment; therefore, it may be of great interest to use automatic measurement approaches that enable rapid and effective process analysis. Artificial intelligence techniques in computer vision and especially deep learning are well suited for this purpose. This article describes the realization of an automatic measurement system based on deep learning for the identification and measurement of morphological parameters of Saccharomyces cerevisiae microorganisms present in brewer's yeast by returning for each of the objects identified within the image the confidence score, the coordinates, and the dimensions of the corresponding ellipsoid-shaped cell. The metrological characteristics of the system have been defined through a calibration process by comparing measurements with a reference system.

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Section: Related Results In the Literaturementioning

confidence: 99%

Use of Artificial Intelligence in optical microscope imaging

Carratù,

Gallo,

Laino

et al. 2023

Proceedings of the 19th IMEKO TC10 International Conference on Measurement for Diagnostics, Optimization and Control on Measure

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“…S11 A and B ). As a comparison, we also classified these cells into three clusters based on their morphological and dynamical features ( Datasets S7–S9 ), which were extracted from the cell images by three representative and well-used conventional image analysis software programs ( 17 ): NIS-Elements ( 18 ), CellProfiler 4 ( 17 ), and TrackMate 7 ( 19 ) in Fiji (ImageJ) ( 20 ) ( SI Appendix , Fig. S12 A , C , and E and Text 7 ).…”

Section: Resultsmentioning

confidence: 99%

Robotic data acquisition with deep learning enables cell image–based prediction of transcriptomic phenotypes

Jin

Ogawa

Hojo

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image–based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.

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