Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800, USA; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
J Neurosci Methods. 2020 Nov 1;345:108852. doi: 10.1016/j.jneumeth.2020.108852. Epub 2020 Aug 6.
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).
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.
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.
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.
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.
脑成像图像处理工作流程的很大一部分是质量控制，通常是通过视觉进行的。质量控制过程中最耗时的步骤之一是将图像分类为清晰或模糊（OOF）。
在本文中，我们介绍了一种从大型数据集（>1.5 PB）中的连续组织切片中自动识别 OOF 脑图像的方法。该方法利用可转向滤波器（STF）为每个图像得出一个焦点值（FV）。FV 结合应用动态阈值的异常值检测，允许对图像进行焦点分类。
通过将我们的算法的结果与对相同图像的视觉检查进行比较，对该方法进行了测试。结果支持该方法的有效性，因为它可以成功识别连续组织切片中的 OOF 图像，并且假阳性数量很少。
我们的算法还与其他方法和指标进行了比较，并在仅包含模拟 OOF 图像的不同图像堆栈中进行了成功测试，以证明该方法对其他大型数据集的适用性。
我们提出了一种实用的方法，可以区分包括连续组织切片的大型数据集中的 OOF 图像，这些图像可以包含在自动化预处理图像分析管道中。
