UNASSIGNED:最新一代的扫描仪可以数字化组织病理学载玻片,以进行计算机图像分析。这些图像包含用于诊断和预后目的的有价值的信息。因此,高数字放大倍数的可用性,如20×和40×通常预计在扫描幻灯片。因此,图像采集通常会生成千兆像素的高分辨率图像,是100,000×100,000像素的倍。自然,这样的大文件的存储和处理可能受到严重的计算瓶颈。因此,对可以在较低放大倍数水平下操作但产生与高放大倍数水平相同的结果的技术的需求变得紧迫。
未经批准:在过去的十年中,超分辨率(SR)的概念已经解决了增强图像分辨率的数字解决方案。此外,深度学习为获取后提高图像分辨率提供了最先进的结果。在这项研究中,针对组织病理学领域训练和评估为图像SR设计的多个深度学习网络。
UNASSIGNED:我们报告了使用公开可用的癌症图像对结果进行定量和定性比较,以揭示深度学习在组织病理学中推断图像分辨率的好处和挑战。三位病理学家评估了结果,以评估生成的SR图像的质量和诊断价值。
未经验证:像素级信息,包括组织病理学图像中的结构和纹理,可以通过深度网络学习;因此,可以通过训练适当的网络来提高扫描幻灯片的分辨率。不同的SR网络可能对各种癌症部位和亚型表现最佳。
UNASSIGNED: The latest generation of scanners can digitize histopathology glass slides for computerized image analysis. These images contain valuable information for diagnostic and prognostic purposes. Consequently, the availability of high digital magnifications like 20 × and 40 × is commonly expected in scanning the slides. Thus, the image acquisition typically generates gigapixel high-resolution images, times as large as 100,000 × 100,000 pixels . Naturally, the storage and processing of such huge files may be subject to severe computational bottlenecks. As a result, the need for techniques that can operate on lower magnification levels but produce results on par with outcomes for high magnification levels is becoming urgent.
UNASSIGNED: Over the past decade, the digital solution of enhancing images resolution has been addressed by the concept of super resolution (SR). In addition, deep learning has offered state-of-the-art results for increasing the image resolution after acquisition. In this
study, multiple deep learning networks designed for image SR are trained and assessed for the histopathology domain.
UNASSIGNED: We report quantitative and qualitative comparisons of the results using publicly available cancer images to shed light on the benefits and challenges of deep learning for extrapolating image resolution in histopathology. Three pathologists evaluated the results to assess the quality and diagnostic value of generated SR images.
UNASSIGNED: Pixel-level information, including structures and textures in histopathology images, are learnable by deep networks; hence improving the resolution quantity of scanned slides is possible by training appropriate networks. Different SR networks may perform best for various cancer sites and subtypes.