Convolutional neural networks for whole slide image superresolution.
Convolutional neural networks for whole slide image superresolution.
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DOI：
10.1364/boe.9.005368
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发表时间：
2018-10
影响因子：
3.4
通讯作者：
L. Mukherjee;A. Keikhosravi;D. Bui;K. Eliceiri
中科院分区：
文献类型：
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作者：
L. Mukherjee;A. Keikhosravi;D. Bui;K. Eliceiri
我们提出了一种计算方法来提高从常见的低放大商业幻灯片扫描仪获取的图像的分辨率的质量。来自这种扫描仪的图像可以廉价地获取，并且在存储和数据传输方面是高效的。然而，它们的质量通常比高分辨率扫描仪和显微镜的图像差，并且没有诊断或临床环境所需的必要分辨率，因此不在这种环境中使用。这项研究提出的主要问题是，这些图像的分辨率是否可以提高，以便与昂贵扫描仪或显微镜的高分辨率图像具有相同的诊断目的。这一需求通常被称为图像处理中的图像超分辨率问题，并得到了广泛的研究。尽管如此，由于这种模式带来的独特挑战，现有的方法中没有一种直接适用于幻灯片扫描仪图像。在这里，我们提出了一种基于卷积神经网络(CNN)的方法，它被专门训练来获取癌症数据的低分辨率幻灯片扫描仪图像，并将其转换为高分辨率图像。我们通过计算分析验证了这些分辨率的改进，以表明增强后的图像提供了相同的定量结果。总而言之，我们广泛的实验表明，该方法确实产生了与高分辨率扫描仪的图像在质量和定量指标上都相似的图像。这种方法为使用低分辨率扫描仪开辟了新的应用可能性，不仅在成本方面，而且在研究和可能的临床应用的扫描途径和速度方面也是如此。
We present a computational approach for improving the quality of the resolution of images acquired from commonly available low magnification commercial slide scanners. Images from such scanners can be acquired cheaply and are efficient in terms of storage and data transfer. However, they are generally of poorer quality than images from high-resolution scanners and microscopes and do not have the necessary resolution needed in diagnostic or clinical environments, and hence are not used in such settings. The driving question of this presented research is whether the resolution of these images could be enhanced such that it would serve the same diagnostic purpose as high-resolution images from expensive scanners or microscopes. This need is generally known as the image super-resolution (SR) problem in image processing, and it has been studied extensively. Even so, none of the existing methods directly work for the slide scanner images, due to the unique challenges posed by this modality. Here, we propose a convolutional neural network (CNN) based approach, which is specifically trained to take low-resolution slide scanner images of cancer data and convert it into a high-resolution image. We validate these resolution improvements with computational analysis to show the enhanced images offer the same quantitative results. In summary, our extensive experiments demonstrate that this method indeed produces images that are similar to images from high-resolution scanners, both in quality and quantitative measures. This approach opens up new application possibilities for using low-resolution scanners, not only in terms of cost but also in access and speed of scanning for both research and possible clinical use.