Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle
Yang Jiao, Hananeh Derakhshan, Barbara St. Pierre Schneider, Emma Regentova, Mei Yang
TL;DR
The paper tackles automated quantification of white blood cells in light microscopic images of injured skeletal muscle by introducing a Localized Iterative Otsu threshold framework combined with muscle edge detection and ROI-based block analysis. This approach addresses background variability and edge-rich backgrounds at 100× magnification, enabling robust discrimination of CD68-positive cells. Evaluations show reduced false positives and improved accuracy compared to ImageJ thresholds, with a dataset of 95 images revealing a peak in CD68-positive cells at 96 hours post-injury and a clear density shift over time. The work offers a practical, automated tool for quantitative muscle healing studies and sets the stage for extending to other protein markers and incorporating machine learning for further accuracy gains.
Abstract
White blood cells (WBCs) are the most diverse cell types observed in the healing process of injured skeletal muscles. In the course of healing, WBCs exhibit dynamic cellular response and undergo multiple protein expression changes. The progress of healing can be analyzed by quantifying the number of WBCs or the amount of specific proteins in light microscopic images obtained at different time points after injury. In this paper, we propose an automated quantifying and analysis framework to analyze WBCs using light microscopic images of uninjured and injured muscles. The proposed framework is based on the Localized Iterative Otsu's threshold method with muscle edge detection and region of interest extraction. Compared with the threshold methods used in ImageJ, the LI Otsu's threshold method has high resistance to background area and achieves better accuracy. The CD68-positive cell results are presented for demonstrating the effectiveness of the proposed work.