Analyzing huge pathology images with open source software
Analyzing huge pathology images with open source software
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DOI：
10.1186/1746-1596-8-92
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发表时间：
2013-06-06
影响因子：
2.6
通讯作者：
Lartaud, Marc
中科院分区：
文献类型：
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作者：
Deroulers, Christophe;Ameisen, David;Lartaud, Marc
背景：数字病理图像越来越多地用于诊断和研究，因为如今切片扫描仪广泛可用，而且对这些图像的定量研究在系统生物学方面产生了新的见解。然而，这种虚拟切片带来了技术挑战，因为图像通常占用数GB的空间，无法在计算机内存中完全打开。此外，没有标准格式。因此，大多数常见的开源工具（如ImageJ）无法处理它们，而其他工具则需要昂贵的硬件，并且仍然慢得令人无法接受。
结果：我们开发了几种跨平台的开源软件工具来克服这些限制。NDPITools提供了一种方法，可将最初采用支持不太好的NDPI格式的显微镜图像转换为一个或多个标准TIFF文件，并以各种TIFF和JPEG格式创建拼接图（将大图像分割成小图像，有重叠或无重叠）。它们可以通过ImageJ插件驱动。LargeTIFFTools对无法装入内存的大型TIFF图像实现了类似的功能。我们在几张数字切片上测试了这些工具的性能，并在适用的情况下将它们与标准软件进行了比较。在一台普通笔记本电脑上对少突胶质细胞瘤组织样本中的细胞进行了一项统计研究，以证明这些工具的效率。
结论：我们的开源软件能够在普通计算机上使用标准软件处理大型图像。它们是跨平台的，独立于专有库，并且非常模块化，允许它们在其他开源项目中使用。它们在执行速度和内存需求方面具有出色的性能。它们为希望研究单张切片的临床医生以及在计算机集群上对许多切片进行图像分析的研究团队或数据中心开辟了有前景的视角。
Background: Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer's memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow.Results: We have developed several cross-platform open source software tools to overcome these limitations. The NDPITools provide a way to transform microscopy images initially in the loosely supported NDPI format into one or several standard TIFF files, and to create mosaics (division of huge images into small ones, with or without overlap) in various TIFF and JPEG formats. They can be driven through ImageJ plugins. The LargeTIFFTools achieve similar functionality for huge TIFF images which do not fit into RAM. We test the performance of these tools on several digital slides and compare them, when applicable, to standard software. A statistical study of the cells in a tissue sample from an oligodendroglioma was performed on an average laptop computer to demonstrate the efficiency of the tools.Conclusions: Our open source software enables dealing with huge images with standard software on average computers. They are cross-platform, independent of proprietary libraries and very modular, allowing them to be used in other open source projects. They have excellent performance in terms of execution speed and RAM requirements. They open promising perspectives both to the clinician who wants to study a single slide and to the research team or data centre who do image analysis of many slides on a computer cluster.