Pollatou Angeliki
Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800, USA; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
J Neurosci Methods. 2020 Jul 15;341:108781. doi: 10.1016/j.jneumeth.2020.108781. Epub 2020 Jun 1.
Whole slide scanners often acquire images of tissue sections that are larger than their field of view through tile or line-scanning. The subsequently stitched or aligned images can often suffer from imaging artifacts such as horizontal or vertical stripes. These stripes degrade the image quality in fluorescent biological imaging samples and can also limit the accuracy of any subsequent analyses such as cell segmentation.
We propose a novel data-driven method of removing stripe artifacts in stitched biological images based on the location of the stripes, background modeling, and illumination correction. This method provides an automated way of removing the stripes of an individual image while preserving image details and quality for subsequent analyses.
The results were assessed using both qualitative and quantitative metrics and the algorithm has proven very effective in removing the stripe artifacts from hundreds of brain images.
Several metrics were used to quantify the effectiveness of our proposed method compared to other published techniques. Images with simulated artifacts were created so that full-reference metrics could be applied to demonstrate the applicability of the algorithm for a wider variety of illumination profiles.
We describe a data analysis pipeline that allows for automatic removal of stripes caused by line-scanning. Our proposed method can be applied without the need for separate blank field of view images or use of image batches to model the background, so it is suitable for real-time parallel batch processing of large datasets.
全玻片扫描仪通常通过拼接或线扫描获取大于其视野范围的组织切片图像。随后拼接或对齐的图像常常会出现诸如水平或垂直条纹之类的成像伪影。这些条纹会降低荧光生物成像样本中的图像质量，还可能限制诸如细胞分割等后续分析的准确性。
我们提出了一种基于条纹位置、背景建模和光照校正的新型数据驱动方法，用于去除拼接生物图像中的条纹伪影。该方法提供了一种自动去除单个图像条纹的方式，同时保留图像细节和质量以用于后续分析。
使用定性和定量指标对结果进行评估，该算法已被证明在去除数百张脑图像的条纹伪影方面非常有效。
使用了几个指标来量化我们提出的方法与其他已发表技术相比的有效性。创建了带有模拟伪影的图像，以便可以应用全参考指标来证明该算法对更广泛光照分布的适用性。
我们描述了一种数据分析流程，可自动去除由线扫描引起的条纹。我们提出的方法无需单独的空白视野图像或使用图像批次来建模背景即可应用，因此适用于大型数据集的实时并行批处理。
