Background: A large part of image processing workflow in brain imaging is quality control which is typically done visually. One of the most time consuming steps of the quality control process is classifying an image as in-focus or out-of-focus (OOF). New method: In this paper we introduce an automated way of identifying OOF brain images from serial tissue sections in large datasets ($>1.5$ PB). The method utilizes steerable filters (STF) to derive a focus value (FV) for each image. The FV combined with an outlier detection that applies a dynamic threshold allows for the focus classification of the images. Results: The method was tested by comparing the results of our algorithm with a visual inspection of the same images. The results support that the method works extremely well by successfully identifying OOF images within serial tissue sections with a minimal number of false positives. Comparison with existing methods: Our algorithm was also compared to other methods and metrics and successfully tested in different stacks of images consisting solely of simulated OOF images in order to demonstrate the applicability of the method to other large datasets. Conclusions: We have presented a practical method to distinguish OOF images from large datasets that include serial tissue sections that can be included in an automated pre-processing image analysis pipeline.

背景：脑成像中图像处理工作流程的很大一部分是质量控制，通常是通过视觉完成的。质量控制过程中最耗时的步骤之一是将图像分类为对焦或失焦 (OOF)。新方法：在本文中，我们介绍了一种从大型数据集中的连续组织切片中识别OOF脑图像的自动化方法（$>1.5$铅）。该方法利用可控滤波器 (STF) 为每个图像导出焦点值 (FV)。FV 与应用动态阈值的异常值检测相结合，可以对图像进行焦点分类。结果：通过将我们的算法的结果与对相同图像的目视检查进行比较来测试该方法。结果支持该方法通过成功识别连续组织切片中的 OOF 图像而工作得非常好，并且误报数量最少。与现有方法的比较：我们的算法还与其他方法和指标进行了比较，并在仅由模拟 OOF 图像组成的不同图像堆栈中成功测试，以证明该方法对其他大型数据集的适用性。结论：