Marker-free cell discrimination by holographic optical tweezers

Schaal, F.; Warber, M.; Zwick, S.; Kuip, Heiko van der; Haist, T.; Osten, W.

doi:10.2971/jeos.2008.09028

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…Firstly, the digital holographic microscopy is applied for accurately detection the axial position of the cells. The second step is cell classification based on combining escape-force measurement [123] with other cell identifications. By combining the two steps in one system, a reliable object detection and tracking system can be obtained.…”

Section: Other Methodsmentioning

confidence: 99%

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Ma¹,

Li²,

Rahaman³

et al. 2021

Preprint

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Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 137 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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Section: Other Methodsmentioning

confidence: 99%