Abstract
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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Acknowledgments
This work was funded in part by HHSN261200800001E from the NCI, 1U24CA180924-01A1 from the NCI, 5R01LM011119-05 and 5R01LM009239-07 from the NLM.
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Baig, F., Mehrotra, M., Vo, H., Wang, F., Saltz, J., Kurc, T. (2016). SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_10
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