Cloud-based whole slide image analysis using mapreduce

Hoang Vo; Jun Kong; Dejun Teng; Yanhui Liang; Ablimit Aji; George Teodoro; Fusheng Wang

doi:10.1007/978-3-319-57741-8_5

Cloud-based whole slide image analysis using mapreduce

Hoang Vo
, Jun Kong
, Dejun Teng
, Yanhui Liang
, Ablimit Aji
, George Teodoro
, Fusheng Wang

Stony Brook University
Emory University
Ohio State University
Hewlett-Packard
Universidade de Brasília

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers
Editors	Lixia Yao, Fusheng Wang, Gang Luo
Publisher	Springer Verlag
Pages	62-67
Number of pages	6
ISBN (Print)	9783319577401
DOIs	https://doi.org/10.1007/978-3-319-57741-8_5
State	Published - 2017
Event	2nd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016 held in conjunction with 42nd International Conference on Very Large Data Bases, VLDB 2016 - New Delhi, India Duration: Sep 5 2016 → Sep 9 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10186 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016 held in conjunction with 42nd International Conference on Very Large Data Bases, VLDB 2016
Country/Territory	India
City	New Delhi
Period	09/5/16 → 09/9/16

Keywords

Cloud computing
MapReduce
Pathology image analysis
Whole slide images

Access to Document

10.1007/978-3-319-57741-8_5

Cite this

Vo, H., Kong, J., Teng, D., Liang, Y., Aji, A., Teodoro, G., & Wang, F. (2017). Cloud-based whole slide image analysis using mapreduce. In L. Yao, F. Wang, & G. Luo (Eds.), Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers (pp. 62-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10186 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-57741-8_5

Vo, Hoang ; Kong, Jun ; Teng, Dejun et al. / Cloud-based whole slide image analysis using mapreduce. Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers. editor / Lixia Yao ; Fusheng Wang ; Gang Luo. Springer Verlag, 2017. pp. 62-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{30581a9d924f40649cb96548da89a585,

title = "Cloud-based whole slide image analysis using mapreduce",

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.",

keywords = "Cloud computing, MapReduce, Pathology image analysis, Whole slide images",

author = "Hoang Vo and Jun Kong and Dejun Teng and Yanhui Liang and Ablimit Aji and George Teodoro and Fusheng Wang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 2nd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016 held in conjunction with 42nd International Conference on Very Large Data Bases, VLDB 2016 ; Conference date: 05-09-2016 Through 09-09-2016",

year = "2017",

doi = "10.1007/978-3-319-57741-8\_5",

language = "English",

isbn = "9783319577401",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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booktitle = "Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers",

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Vo, H, Kong, J, Teng, D, Liang, Y, Aji, A, Teodoro, G & Wang, F 2017, Cloud-based whole slide image analysis using mapreduce. in L Yao, F Wang & G Luo (eds), Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10186 LNCS, Springer Verlag, pp. 62-67, 2nd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016 held in conjunction with 42nd International Conference on Very Large Data Bases, VLDB 2016, New Delhi, India, 09/5/16. https://doi.org/10.1007/978-3-319-57741-8_5

Cloud-based whole slide image analysis using mapreduce. / Vo, Hoang; Kong, Jun; Teng, Dejun et al.
Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers. ed. / Lixia Yao; Fusheng Wang; Gang Luo. Springer Verlag, 2017. p. 62-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10186 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cloud-based whole slide image analysis using mapreduce

AU - Vo, Hoang

AU - Kong, Jun

AU - Teng, Dejun

AU - Liang, Yanhui

AU - Aji, Ablimit

AU - Teodoro, George

AU - Wang, Fusheng

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Cloud computing

KW - MapReduce

KW - Pathology image analysis

KW - Whole slide images

UR - https://www.scopus.com/pages/publications/85018680168

U2 - 10.1007/978-3-319-57741-8_5

DO - 10.1007/978-3-319-57741-8_5

M3 - Conference contribution

AN - SCOPUS:85018680168

SN - 9783319577401

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 62

EP - 67

BT - Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers

A2 - Yao, Lixia

A2 - Wang, Fusheng

A2 - Luo, Gang

PB - Springer Verlag

T2 - 2nd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016 held in conjunction with 42nd International Conference on Very Large Data Bases, VLDB 2016

Y2 - 5 September 2016 through 9 September 2016

ER -

Vo H, Kong J, Teng D, Liang Y, Aji A, Teodoro G et al. Cloud-based whole slide image analysis using mapreduce. In Yao L, Wang F, Luo G, editors, Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers. Springer Verlag. 2017. p. 62-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-57741-8_5

Cloud-based whole slide image analysis using mapreduce

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Keywords

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