Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging

2025-10-20

AI Summary

Existing methods struggle to automatically identify irregularly shaped, compositionally heterogeneous circulating cell clusters (CCCs), particularly in multi-channel fluorescence microscopy, where they are highly susceptible to debris and staining artifacts. To address this, we propose the first computational framework integrating bright-field and multi-channel fluorescence imaging: (1) fine-tuned YOLOv11 for high-precision CCC detection; (2) a novel fluorescence channel fusion and morphological contour integration strategy to resolve intra-cluster cellular composition and spatial organization; and (3) synergistic integration with conventional flow cytometry data to enhance biological interpretability. Evaluated on real human blood samples, our method achieves >95% accuracy in both CCC classification and phenotypic identification—substantially outperforming state-of-the-art approaches. This framework enables the first end-to-end automated analysis of CCCs in multi-channel flow imaging, establishing a scalable, biologically grounded paradigm for studying CCCs in thrombosis, infection, and tumor microenvironments.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Automatically classify circulating blood cell clusters using multi-channel imaging
• Address irregular shapes and heterogeneous cell types in clusters
• Develop computational framework for cluster identification and cell typing

Innovation

• Fine-tunes YOLOv11 model for cell cluster classification
• Overlays cluster contours with multi-channel fluorescence stains
• Achieves over 95% accuracy in classification and identification