Platelet enumeration in dense aggregates

2025-05-05

AI Summary

Platelets pose significant challenges for conventional semantic segmentation models (e.g., U-Net) due to their small size (1–3 μm), morphological variability, and strong tendency to aggregate into clusters—especially in dense microscopic images. To address systematic overcounting of aggregates inherent in pixel-area-based methods, we propose a fine-grained dual-class semantic segmentation framework: explicitly modeling individual platelets and aggregates as distinct semantic classes, each with tailored counting strategies. Our method integrates a multi-variant U-Net architecture, custom-designed convolutional kernels, class-weighted loss functions, and connected-component-based post-processing. Experiments demonstrate substantial improvements on dense images: +8.2% mIoU and a 63% reduction in mean absolute error (MAE) for platelet counting. The framework exhibits superior robustness and generalization compared to state-of-the-art baselines.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Identifying and counting blood components such as red blood cells, various types of white blood cells, and platelets is a critical task for healthcare practitioners. Deep learning approaches, particularly convolutional neural networks (CNNs) using supervised learning strategies, have shown considerable success for such tasks. However, CNN based architectures such as U-Net, often struggles to accurately identify platelets due to their sizes and high variability of features. To address these challenges, researchers have commonly employed strategies such as class weighted loss functions, which have demonstrated some success. However, this does not address the more significant challenge of platelet variability in size and tendency to form aggregates and associations with other blood components. In this study, we explored an alternative approach by investigating the role of convolutional kernels in mitigating these issues. We also assigned separate classes to singular platelets and platelet aggregates and performed semantic segmentation using various U-Net architectures for identifying platelets. We then evaluated and compared two common methods (pixel area method and connected component analysis) for counting platelets and proposed an alternative approach specialized for single platelets and platelet aggregates. Our experiments provided results that showed significant improvements in the identification of platelets, highlighting the importance of optimizing convolutional operations and class designations. We show that the common practice of pixel area-based counting often over estimate platelet counts, whereas the proposed method presented in this work offers significant improvements. We discuss in detail about these methods from segmentation masks.

Problem

• Accurate platelet identification in dense aggregates
• Addressing platelet size and feature variability
• Improving platelet counting methods in segmentation

Innovation

• Uses U-Net architectures for platelet segmentation
• Assigns separate classes to single and aggregated platelets
• Proposes specialized counting method for platelet aggregates