Platelet enumeration in dense aggregates

TL;DR

This paper improves platelet detection in dense blood aggregates using optimized convolutional kernels and specialized class designations in semantic segmentation, outperforming traditional pixel area counting methods.

Contribution

The study introduces a novel approach with optimized convolutional kernels and separate classes for platelets and aggregates, enhancing segmentation accuracy.

Findings

Significant improvement in platelet identification accuracy.

Pixel area counting tends to overestimate platelet counts.

Proposed method outperforms traditional counting approaches.

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.