自动量化肌肉损伤后白细胞数量，提升显微图像分析精度
Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle
- 基于局部迭代Otsu法结合肌组织边缘检测定位感兴趣区域
- 相比ImageJ常用方法，背景干扰抵抗能力强，准确率显著提升
- 适用于肌肉修复研究中的白细胞动态变化分析
白细胞（WBCs）是骨骼肌损伤后修复过程中最多样化的细胞类型，在愈合过程中表现出动态的细胞反应和多重蛋白表达变化。通过在不同时间点获取的光镜图像中量化WBC数量或特定蛋白含量，可分析修复进程。本文提出一种自动化定量分析框架，利用未损伤及损伤肌肉的光镜图像分析WBC。该框架基于局部迭代Otsu阈值法，结合肌肉边缘检测与感兴趣区域提取。相较于ImageJ中使用的阈值方法，本方法对背景区域具有更强的鲁棒性，实现了更高精度。通过展示CD68阳性细胞的结果，验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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