Context: Analyzing digital pathology images is necessary to draw diagnostic conclusions by investigating tissue patterns and cellular morphology. However, manual evaluation can be time-consuming, expensive, and prone to inter- and intra-observer variability. Objective: To assist pathologists using computerized solutions, automated tissue structure detection and segmentation must be proposed. Furthermore, generating pixel-level object annotations for histopathology images is expensive and time-consuming. As a result, detection models with bounding box labels may be a feasible solution. Design: This paper studies. YOLO-v4 (You-Only-Look-Once), a real-time object detector for microscopic images. YOLO uses a single neural network to predict several bounding boxes and class probabilities for objects of interest. YOLO can enhance detection performance by training on whole slide images. YOLO-v4 has been used in this paper. for glomeruli detection in human kidney images. Multiple experiments have been designed and conducted based on different training data of two public datasets and a private dataset from the University of Michigan for fine-tuning the model. The model was tested on the private dataset from the University of Michigan, serving as an external validation of two different stains, namely hematoxylin and eosin (H&E) and periodic acid-Schiff (PAS). Results: Average specificity and sensitivity for all experiments, and comparison of existing segmentation methods on the same datasets are discussed. Conclusions: Automated glomeruli detection in human kidney images is possible using modern AI models. The design and validation for different stains still depends on variability of public multi-stain datasets.

翻译：背景：通过分析组织模式和细胞形态学特征来得出诊断结论，必须对数字病理图像进行分析。然而，人工评估不仅耗时费力、成本高昂，且易受观察者间和观察者自身变异性的影响。目的：为辅助病理学家采用计算机化解决方案，需要提出自动化的组织结构检测与分割方法。此外，为组织病理学图像生成像素级目标标注既昂贵又耗时，因此采用带有边界框标注的检测模型可能是可行的解决方案。设计：本文研究YOLO-v4（You-Only-Look-Once，即"你只看一次"）这一应用于显微图像的实时目标检测器。YOLO利用单一神经网络预测感兴趣目标的多个边界框及其类别概率，通过在全切片图像上训练可提升检测性能。本文采用YOLO-v4进行人肾脏图像中的肾小球检测。基于两个公开数据集和密歇根大学私有数据集的不同训练数据，设计并开展了多组模型微调实验。模型在密歇根大学私有数据集上进行测试，该数据集包含苏木精-伊红（H&E）和过碘酸雪夫（PAS）两种染色方案，作为外部验证。结果：讨论了所有实验的平均特异性和灵敏度，并与现有分割方法在相同数据集上的效果进行了比较。结论：采用现代AI模型可实现人肾脏图像中肾小球的自动检测。针对不同染色方案的设计与验证仍取决于公开多染色数据集的变异性。