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MaRelA: a cloud MapReduce based high performance whole slide image analysis framework

机译:MaRelA:基于云MapReduce的高​​性能整体幻灯片图像分析框架

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Recent advancements in systematic analysis of high resolution whole slide images have increase efficiency of diagnosis, prognosis and prediction of cancer and important diseases. Due to the enormous sizes and dimensions of whole slide images, the analysis requires extensive computing resources which are not commonly available. Images have to be tiled for processing due to computer memory limitations, which lead to inaccurate results due to the ignorance of boundary crossing objects. Thus, we propose a generic and highly scalable cloud-based image analysis framework for whole slide images. The framework enables parallelized integration of image analysis steps, such as segmentation and aggregation of micro-structures in a single pipeline, and generation of final objects manageable by databases. The core concept relies on the abstraction of objects in whole slide images as different classes of spatial geometries, which in turn can be handled as text based records in MapReduce. The framework applies an overlapping partitioning scheme on images, and provides parallelization of tiling and image segmentation based on MapReduce architecture. It further provides robust object normalization, graceful handling of boundary objects with an efficient spatial indexing based matching method to generate accurate results. Our experiments on Amazon EMR show that MaReIA is highly scalable, generic and extremely cost effective by benchmark tests.
机译:对高分辨率全幻灯片图像进行系统分析的最新进展提高了癌症和重要疾病的诊断,预后和预测效率。由于整个幻灯片图像的尺寸巨大,因此分析需要大量的计算资源,而这些资源通常是不可用的。由于计算机内存的限制,必须对图像进行平铺处理,由于对边界物体的无知,导致结果不准确。因此,我们为整个幻灯片图像提出了一个通用且高度可扩展的基于云的图像分析框架。该框架实现了图像分析步骤的并行集成,例如在单个管道中细分和聚合微结构,以及生成可由数据库管理的最终对象。核心概念依赖于将整个幻灯片图像中的对象抽象为不同类别的空间几何,然后可以将其作为MapReduce中基于文本的记录来处理。该框架在图像上应用了重叠分区方案,并基于MapReduce体系结构提供了切片和图像分割的并行化。它还提供了强大的对象归一化,边界对象的优美处理,以及基于空间索引的高效匹配方法来生成准确的结果。我们在Amazon EMR上的实验表明,通过基准测试,MaReIA具有高度的可扩展性,通用性和极高的成本效益。

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