SparkGIS:组织图像分析研究中算法结果的有效比较与评估

Furqan Baig, Mudit Mehrotra, Hoang Vo, Fusheng Wang, Joel Saltz, Tahsin Kurc
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引用次数: 0

摘要

算法评估提供了一种方法来描述不同图像分析算法之间的变异性,通过比较多种结果来验证算法,并促进算法灵敏度研究。病理图像分析中图像和分析结果的规模给算法评估带来了巨大挑战。我们提出的 SparkGIS 是一种分布式、内存空间数据处理框架,用于查询、检索和比较大量分析图像结果数据,以进行算法评估。我们的方法结合了 Apache Spark 的内存分布式处理能力和 Hadoop-GIS 的高效空间查询处理能力。SparkGIS 用于热图计算的实验评估表明,SparkGIS 高效且可扩展,能够在大型数据集上进行算法评估和算法敏感性研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies.

SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies.

SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies.

Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison of multiple results, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes of analytical image result data for algorithm evaluation. Our approach combines the in-memory distributed processing capabilities of Apache Spark and the efficient spatial query processing of Hadoop-GIS. The experimental evaluation of SparkGIS for heatmap computations used to compare nucleus segmentation results from multiple images and analysis runs shows that SparkGIS is efficient and scalable, enabling algorithm evaluation and algorithm sensitivity studies on large datasets.

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