Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy

Fang, Teng; Yuan, Pengbo; Gong, Chen; Jiang, Yueping; Yu, Yuezhou; Shang, Wenhao; Tian, Chan; Ye, Anpei

doi:10.1039/d2an00024e

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…Integrating QPI label-free data with AI, such as phase imaging with computational specificity (PICS), enables precise cell-specific extraction and rapid leukocyte detection ( 16 ). Lastly, leveraging deep learning for visual target identification offers novel approaches to myeloid cell detection ( 17 ).…”

Section: Automation Of Bone Marrow Cell Morphology Detectionmentioning

confidence: 99%

“…Rosenberg et al ( 29 ) demonstrated a novel label-free approach that integrates image recognition and Raman spectroscopy to swiftly identify nucleated red blood cells in blood samples. Concurrently, their research revealed that the visual object detection deep learning algorithm YOLOv3 exhibits superior real-time detection capabilities on economical computer setups for recognizing cell morphology, albeit with a lower precision ( 17 ). Imaging flow cytometry, as a robust and objective tool, enables the simultaneous analysis of phenotypic parameters alongside image-based morphological features like cell size and nuclearity, thereby potentially mitigating inter-observer discrepancies.…”

Section: Automation Of Bone Marrow Cell Morphology Identificationmentioning

confidence: 99%

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Recent advancements in machine learning for bone marrow cell morphology analysis

Lin,

Chen,

Chen

2024

Front. Med.

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As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.

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Section: Automation Of Bone Marrow Cell Morphology Detectionmentioning

confidence: 99%

Section: Automation Of Bone Marrow Cell Morphology Identificationmentioning

confidence: 99%

Recent advancements in machine learning for bone marrow cell morphology analysis

Lin,

Chen,

Chen

2024

Front. Med.

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“…Fang, T. et al combined image recognition and Raman spectroscopy to quickly identify rare cells in the blood. And studied the visual target detection method Faster RCNN and YOLOv3 under deep learning, and the study showed that the YOLOv3 detection effect is better [19]. Panda P. et al proposed a conditional deep learning method to recognize difficult images by convolutional layer features and finally achieve better recognition with the activation of the deeper network [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Image Recognition Methods Based on Deep Learning

Xu,

Li,

Wang

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, deep learning is used to study image recognition techniques. Firstly, the image recognition process is structured, the YOLOv4 network framework is constructed, the features are extracted using the PANet reinforcement network, and the image overlap is extracted using the loss function. Then, we make an improved architecture ACDNet algorithm based on YOLOv4 and set the main function of the ACDNet model. Finally, the accuracy of image recognition under different algorithms and the recognition effect evaluation of the ACDNet algorithm are tested, respectively. The study shows that the image recognition accuracy of the ACDNet algorithm is located in the first of the three algorithms, with the highest accuracy of 98.16%, which is good and effective for image recognition and classification. The accuracy of ACDNet in the training set of plant image recognition is 99.34%, which is good for classification and recognition performance.

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“…Fang et al proposed a fast and label-free recognition method for nucleated red blood cells based on deep learning object detection and single-cell Raman spectroscopy. 25 All the above cell detection methods based on the deep learning algorithm can only distinguish two types of cells. The evaluation of red blood cell quality based on morphology is mainly based on the dynamic and continuous morphology changes of stored RBCs in different periods.…”

Section: Introductionmentioning

confidence: 99%

Blood quality evaluation via on-chip classification of cell morphology using a deep learning algorithm

et al. 2023

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Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy

Abstract: Nucleated red blood cell (NRBC) as a type of rare cells present in an adult’s peripheral blood is concerned in hematology, intensive care medicine and prenatal diagnostics. However, it is...

Cited by 8 publications

References 29 publications

Recent advancements in machine learning for bone marrow cell morphology analysis

Recent advancements in machine learning for bone marrow cell morphology analysis

Research on Image Recognition Methods Based on Deep Learning

Blood quality evaluation via on-chip classification of cell morphology using a deep learning algorithm

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