Out-of-focus brain image detection in serial tissue sections.
Out-of-focus brain image detection in serial tissue sections.
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DOI：
10.1016/j.jneumeth.2020.108852
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发表时间：
2020-11-01
影响因子：
3
通讯作者：
Ferrante, Daniel D.
中科院分区：
文献类型：
--
作者：
Pollatou, Angeliki;Ferrante, Daniel D.
关键词：
在脑成像的图像处理工作流程中，很大一部分是质量控制，通常是视觉上完成的。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）。本文介绍了一种从大数据集（> 1.5 PB）的连续组织切片中自动识别OOF脑图像的方法。该方法利用可操纵滤波器（STF）来获得每个图像的焦点值（FV）。FV与应用动态阈值的离群值检测相结合，允许对图像进行焦点分类。通过将我们的算法结果与相同图像的视觉检查结果进行比较，对该方法进行了测试。结果表明，该方法通过成功地识别序列组织切片中的OOF图像，并以最少的误报率成功地工作得非常好。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，并成功地在仅由模拟OOF图像组成的不同图像堆栈中进行了测试，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来区分OOF图像和大型数据集，这些数据集包括可以包含在自动预处理图像分析管道中的串行组织切片。
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. 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. 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.
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