Department of Industrial Engineering, College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA.
Department of Industrial Engineering, College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA; H. Milton Stewart School of Industrial and Systems Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Microvasc Res. 2024 Nov;156:104732. doi: 10.1016/j.mvr.2024.104732. Epub 2024 Aug 13.
Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and brain of living subjects. In medical imaging, edge detection is used to accurately identify and delineate important structures and boundaries inside the images. To improve edge sharpness, edge detection frequently requires the inclusion of low-level features. Herein, a machine learning approach is needed to automate the edge detection of multicellular aggregates of distinctly labeled blood cells within the microcirculation. In this work, the Structured Adaptive Boosting Trees algorithm (AdaBoost.S) is proposed as a contribution to overcome some of the edge detection challenges related to medical images. Algorithm design is based on the observation that edges over an image mask often exhibit special structures and are interdependent. Such structures can be predicted using the features extracted from a bigger image patch that covers the image edge mask. The proposed AdaBoost.S is applied to detect multicellular aggregates within blood vessels from the fluorescence lung intravital images of mice exposed to e-cigarette vapor. The predictive capabilities of this approach for detecting platelet-neutrophil aggregates within the lung blood vessels are evaluated against three conventional machine learning algorithms: Random Forest, XGBoost and Decision Tree. AdaBoost.S exhibits a mean recall, F-score, and precision of 0.81, 0.79, and 0.78, respectively. Compared to all three existing algorithms, AdaBoost.S has statistically better performance for recall and F-score. Although AdaBoost.S does not outperform Random Forest in precision, it remains superior to the XGBoost and Decision Tree algorithms. The proposed AdaBoost.S is widely applicable to analysis of other fluorescence intravital microscopy applications including cancer, infection, and cardiovascular disease.
荧光活体显微镜可捕获大量动态多细胞相互作用的数据，这些数据来自于活体动物的各种器官，如肺、肝和脑。在医学成像中，边缘检测用于准确识别和描绘图像内部的重要结构和边界。为了提高边缘锐度，边缘检测通常需要包含低水平特征。在此，需要机器学习方法来自动检测微循环中明显标记的血细胞的多细胞聚集体的边缘。在这项工作中，提出了结构化自适应提升树算法（AdaBoost.S），以克服与医学图像相关的一些边缘检测挑战。算法设计基于这样的观察结果，即图像掩模上的边缘通常表现出特殊的结构且相互依赖。可以使用覆盖图像边缘掩模的更大图像补丁中提取的特征来预测这些结构。所提出的 AdaBoost.S 应用于从暴露于电子烟蒸气的小鼠的荧光活体肺图像中检测血管内的多细胞聚集体。该方法在检测肺血管内血小板-中性粒细胞聚集体方面的预测能力与三种传统机器学习算法（随机森林、XGBoost 和决策树）进行了评估。AdaBoost.S 的平均召回率、F 分数和精度分别为 0.81、0.79 和 0.78。与所有三种现有算法相比，AdaBoost.S 在召回率和 F 分数方面具有统计学上更好的性能。虽然 AdaBoost.S 在精度方面不如随机森林，但它仍然优于 XGBoost 和决策树算法。所提出的 AdaBoost.S 广泛适用于包括癌症、感染和心血管疾病在内的其他荧光活体显微镜应用的分析。
