Automatic cell counting from stimulated Raman imaging using deep learning

Zhang, Qianqian; Yun, Kyung Keun; Wang, Hao; Yoon, Sang Won; Lü, Fan; Won, Daehan

doi:10.1371/journal.pone.0254586

Cited by 9 publications

(3 citation statements)

References 59 publications

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“…These methods have shown superior performance in terms of accuracy and speed compared to other methods. They have shown remarkable results in various other domains also, particularly in medical image analysis [ 63 , 64 , 65 ]. Table 1 summarizes the performance of the three methods in terms of advantages and limitations and also highlights their applications in cell detection and counting.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2023

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“…These studies have used deep learning networks to classify malignant tissue from healthy tissue and to classify activated lymphocytes, but not for cell counting. Automated cell counting based on U-net segmentation was demonstrated on label-free stimulated Raman scattering images of human brain tumors [ 18 ]. Regression based counting generates cell counts based directly on the entire input image, therefore avoiding the need to annotate cells beforehand.…”

Section: Introductionmentioning

confidence: 99%

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

et al. 2023

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Background In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and time-consuming. Studies have shown promising leukocyte identification performance on blood fractions, using third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. Objective To extend leukocyte differentiation to BALF samples using THG/MPEF microscopy, and to show the potential of a trained deep learning algorithm for automated leukocyte identification and quantification. Methods Leukocytes from blood obtained from three healthy individuals and one asthma patient, and BALF samples from six ILD patients were isolated and imaged using label-free microscopy. The cytological characteristics of leukocytes, including neutrophils, eosinophils, lymphocytes, and macrophages, in terms of cellular and nuclear morphology, and THG and MPEF signal intensity, were determined. A deep learning model was trained on 2D images and used to estimate the leukocyte ratios at the image-level using the differential cell counts obtained using standard cytological techniques as reference. Results Different leukocyte populations were identified in BALF samples using label-free microscopy, showing distinctive cytological characteristics. Based on the THG/MPEF images, the deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, reaching >90% accuracy on BALF samples in the hold-out testing set. Conclusions Label-free THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios has potential to speed-up the diagnostic process and to reduce costs, workload and inter-observer variations.

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“…These studies have used deep learning networks to classify malignant tissue from healthy tissue and to classify activated lymphocytes, but not for cell counting. Automated cell counting based on U-net segmentation was demonstrated on label-free stimulated Raman scattering images of human brain tumors [17]. Regression based counting generates cell counts based directly on the entire input image, therefore avoiding the need to annotate cells beforehand.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of leukocytes in bronchoalveolar lavage fluid samples using higher harmonic generation microscopy and deep learning

Huizen

Blokker

Rip

et al. 2022

Preprint

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In many diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. In BALF the differentiation of neutrophils, eosinophils, lymphocytes, and macrophages can contribute to diagnose the underlying ILD entity. To analyze the BALF standard cytological techniques are labor-intensive and time-consuming. Studies have shown promising cell identification performance on blood fractions analyzed by third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. Here, we extend this to BALF samples, and we trained a deep learning algorithm for automated analysis on the image level against reference cytology. We imaged blood fractions from three healthy individuals and one asthma patients, and six BALFs from ILD patients. We determined the leukocyte characteristics in terms of cellular and nuclear morphology, and THG and MPEF signal intensity. A deep learning model was trained on both blood fractions and BALF 2D images was used to estimate the leukocyte ratios by using only the standard cytology differential cell ratios at the image-level as reference. The deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, coming within a 2 to 10% margin in BALF samples in the hold-out testing set. We suggest that the performance of the combined label free imaging and AI analysis can be improved further by collecting 3D data and data of additional fluid samples of various ILD diseases and healthy samples, THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios will not only speed-up the diagnostic process but can also reduce costs, the workload and reduce inter-observer variations.

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Automatic cell counting from stimulated Raman imaging using deep learning

Cited by 9 publications

References 59 publications

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

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

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

Differentiation of leukocytes in bronchoalveolar lavage fluid samples using higher harmonic generation microscopy and deep learning

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