Abstract
Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells(WBCs), and platelets are significant biomarkers linked to conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters often exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. This study introduces a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once(YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, enhancing accuracy despite cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.
Abstract (translated)
含红细胞（RBC）、白细胞（WBC）和血小板的循环血液细胞团簇（CCC）是与血栓形成、感染和炎症等状况相关的显著生物标志物。流式细胞术结合荧光染色常用于分析这些细胞团簇，揭示细胞形态和蛋白质谱型。虽然基于机器学习的方法在单个细胞流式细胞图像自动分析方面取得了进展，但缺乏构建可用于自动分析包含CCC的图像工具的努力。与单独的细胞不同，细胞团簇通常表现出不规则形状和大小，并且这些细胞团簇往往由不同的细胞类型组成，需要使用多通道染色来识别团簇内的特定细胞类型。 本研究介绍了一种新的计算框架，用于分析CCC图像并确定集群中的细胞类型。该框架采用两步分析策略：首先通过微调You Only Look Once（YOLOv11）模型将图像分类为细胞团簇和非团簇组，这种方法优于传统的卷积神经网络（CNNs）和视觉变压器（ViT）。然后，它通过在多通道荧光染色区域上叠加群集轮廓来识别细胞类型，从而即使在存在细胞碎片和染色伪影的情况下也能提高准确性。这一方法在集群分类和表型鉴定方面均达到了超过95%的准确率。 总之，我们的自动化框架有效地分析了流式细胞术产生的CCC图像，结合了明场和荧光数据。最初用于血细胞测试后，它具有更广泛的应用潜力，例如分析免疫和肿瘤细胞团簇，支持各种疾病背景下的细胞研究。
URL
https://arxiv.org/abs/2510.17716
