Automated Counting via Multicolumn Network and CytoSMART Exact FL Microscope

Flórez, Sebastián López; González‐Briones, Alfonso; Hernández, Guillermo; Prieta, Fernando De la

doi:10.1007/978-3-031-22356-3_20

Cited by 1 publication

(1 citation statement)

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“…Deep learning-based approaches for counting are more precise and reproducible than conventional approaches because they can handle a wide range of objects with varying kinds, sizes, and complex materials and textures [ 29 ]. When we look at the literature on cell counting using automated tools, the bulk of the methods that are currently being used, however, rely on segmentation-based tactics, which call for a lot of training, tuning, and parameter optimization [ 30 , 31 , 32 , 33 ]. These techniques utilize image processing algorithms such as edge detection, thresholding, morphological operations, and watershed segmentation to separate cells from the background and from each other [ 34 , 35 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

et al. 2023

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Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.

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