A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology
A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology
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DOI：
10.3389/fmed.2019.00193
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发表时间：
2019-09-30
影响因子：
3.9
通讯作者：
Pozidis, Haralampos
中科院分区：
文献类型：
--
作者：
Anghel, Andreea;Stanisavljevic, Milos;Pozidis, Haralampos
染色标准化是现代数字病理学中计算机辅助诊断（CAD）系统的重要处理任务。该任务减少了来自不同实验室的染色图像中存在的颜色和强度变化。因此，染色归一化通常增加CAD系统的预测精度。然而，存在该归一化步骤必须克服的计算挑战，特别是对于实时应用：与高分辨率图像处理相关联的存储器和运行时瓶颈，例如，40倍此外，染色归一化可能对输入图像的质量敏感，例如，当它们含有污点或污垢时。在这种情况下，算法可能无法准确地估计染色矢量。我们提出了一个高性能的系统染色正常化使用一个国家的最先进的无监督方法的基础上染色矢量估计。使用高度优化的归一化引擎，我们的架构可以高速和大规模处理高分辨率的全切片图像。这个优化的引擎集成了自动阈值技术来确定有用的像素，并使用了一种新的像素采样方法，显着减少了归一化算法的处理时间。我们展示了我们的架构的性能，使用不同大小的图像和扫描仪格式，属于四个不同的数据集的测量。结果表明，与基线实现相比，我们的优化实现了高达58倍的加速。我们还证明了我们的系统的可扩展性，通过显示，处理时间尺度与图像中存在的组织像素的量几乎呈线性关系。此外，我们表明，归一化算法的输出可能会受到不利影响时，输入图像包括文物。为了解决这个问题，我们通过引入参数交叉检查技术来增强染色归一化管道，该技术自动检测算法的关键参数的失真。为了评估所提出的方法的鲁棒性，我们采用了一个机器学习（ML）管道，该管道对用于检测前列腺癌的图像进行分类。结果表明，增强的归一化算法提高了ML管道的分类精度，在存在低质量的输入图像。对于示例性ML管道，我们的新方法将不可见数据集的准确率从0.79提高到0.87。
Stain normalization is an important processing task for computer-aided diagnosis (CAD) systems in modern digital pathology. This task reduces the color and intensity variations present in stained images from different laboratories. Consequently, stain normalization typically increases the prediction accuracy of CAD systems. However, there are computational challenges that this normalization step must overcome, especially for real-time applications: the memory and run-time bottlenecks associated with the processing of images in high resolution, e.g., 40X. Moreover, stain normalization can be sensitive to the quality of the input images, e.g., when they contain stain spots or dirt. In this case, the algorithm may fail to accurately estimate the stain vectors. We present a high-performance system for stain normalization using a state-of-the-art unsupervised method based on stain-vector estimation. Using a highly-optimized normalization engine, our architecture enables high-speed and large-scale processing of high-resolution whole-slide images. This optimized engine integrates an automated thresholding technique to determine the useful pixels and uses a novel pixel-sampling method that significantly reduces the processing time of the normalization algorithm. We demonstrate the performance of our architecture using measurements from images of different sizes and scanner formats that belong to four different datasets. The results show that our optimizations achieve up to 58x speedup compared to a baseline implementation. We also prove the scalability of our system by showing that the processing time scales almost linearly with the amount of tissue pixels present in the image. Furthermore, we show that the output of the normalization algorithm can be adversely affected when the input images include artifacts. To address this issue, we enhance the stain normalization pipeline by introducing a parameter cross-checking technique that automatically detects the distortion of the algorithm's critical parameters. To assess the robustness of the proposed method we employ a machine learning (ML) pipeline that classifies images for detection of prostate cancer. The results show that the enhanced normalization algorithm increases the classification accuracy of the ML pipeline in the presence of poor-quality input images. For an exemplary ML pipeline, our new method increases the accuracy on an unseen dataset from 0.79 to 0.87.