Abstract
Systematic analysis of high resolution whole slide images enables more effective diagnosis, prognosis and prediction of cancer and other 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 divided into smaller regions for processing due to computer memory limitations, which will lead to inaccurate results due to the ignorance of boundary crossing objects. In this paper, we propose a highly scalable and cost effective MapReduce based image analysis framework for whole slide image processing, and provide a cloud based implementation. The framework takes a grid-based overlapping partitioning scheme, and provides parallelization of image segmentation based on MapReduce. It provides graceful handling of boundary objects with a highly efficient spatial indexing based matching method, thus avoiding loss of accuracy due to partitioning. We demonstrate that the system achieves high scalability and is cost-effective – our experiments demonstrate that it costs less than fifteen cents to analyze one image on average using Amazon Elastic MapReduce.
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Acknowledgements
This work is supported in part by NSF IIS 1350885, by NSF ACI 1350885, by Grant Number K25CA181503 from the National Institute of Health, by Grant Number R01LM009239 from the National Library of Medicine, by Grant Number 1U24CA180924-01A1 from the National Cancer Institute, and by CNPq.
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Vo, H. et al. (2017). Cloud-Based Whole Slide Image Analysis Using MapReduce. In: Wang, F., Yao, L., Luo, G. (eds) Data Management and Analytics for Medicine and Healthcare. DMAH 2016. Lecture Notes in Computer Science(), vol 10186. Springer, Cham. https://doi.org/10.1007/978-3-319-57741-8_5
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DOI: https://doi.org/10.1007/978-3-319-57741-8_5
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