High performance computing techniques for scaling image analysis workflows

Patrick M. Widener; Tahsin Kurc; Wenjin Chen; Fusheng Wang; Lin Yang; Jun Hu; Vijay Kumar; Vicky Chu; Lee Cooper; Jun Kong; Ashish Sharma; Tony Pan; Joel H. Saltz; David J. Foran

doi:10.1007/978-3-642-28145-7_7

High performance computing techniques for scaling image analysis workflows

Patrick M. Widener, Tahsin Kurc, Wenjin Chen, Fusheng Wang, Lin Yang, Jun Hu, Vijay Kumar, Vicky Chu, Lee Cooper, Jun Kong, Ashish Sharma, Tony Pan, Joel H. Saltz, David J. Foran

Workflow and Ecosystem Services

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

1 Scopus citations

Abstract

Biomedical images are intrinsically complex with each domain and modality often requiring specialized knowledge to accurately render diagnosis and plan treatment. A general software framework that provides access to high-performance resources can make possible high-throughput investigations of micro-scale features as well as algorithm design, development and evaluation. In this paper we describe the requirements and challenges of supporting microscopy analyses of large datasets of high-resolution biomedical images. We present high-performance computing approaches for storage and retrieval of image data, image processing, and management of analysis results for additional explorations. Lastly, we describe issues surrounding the use of high performance computing for scaling image analysis workflows.

Original language	English
Title of host publication	Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers
Pages	67-77
Number of pages	11
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-28145-7_7
State	Published - 2012
Event	10th International Conference on Applied Parallel and Scientific Computing, PARA 2010 - Reykjavik, Iceland Duration: Jun 6 2010 → Jun 9 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7134 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Applied Parallel and Scientific Computing, PARA 2010
Country/Territory	Iceland
City	Reykjavik
Period	06/6/10 → 06/9/10

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10.1007/978-3-642-28145-7_7

Cite this

Widener, P. M., Kurc, T., Chen, W., Wang, F., Yang, L., Hu, J., Kumar, V., Chu, V., Cooper, L., Kong, J., Sharma, A., Pan, T., Saltz, J. H., & Foran, D. J. (2012). High performance computing techniques for scaling image analysis workflows. In Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers (PART 2 ed., pp. 67-77). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7134 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-28145-7_7

Widener, Patrick M. ; Kurc, Tahsin ; Chen, Wenjin et al. / High performance computing techniques for scaling image analysis workflows. Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers. PART 2. ed. 2012. pp. 67-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "High performance computing techniques for scaling image analysis workflows",

abstract = "Biomedical images are intrinsically complex with each domain and modality often requiring specialized knowledge to accurately render diagnosis and plan treatment. A general software framework that provides access to high-performance resources can make possible high-throughput investigations of micro-scale features as well as algorithm design, development and evaluation. In this paper we describe the requirements and challenges of supporting microscopy analyses of large datasets of high-resolution biomedical images. We present high-performance computing approaches for storage and retrieval of image data, image processing, and management of analysis results for additional explorations. Lastly, we describe issues surrounding the use of high performance computing for scaling image analysis workflows.",

author = "Widener, \{Patrick M.\} and Tahsin Kurc and Wenjin Chen and Fusheng Wang and Lin Yang and Jun Hu and Vijay Kumar and Vicky Chu and Lee Cooper and Jun Kong and Ashish Sharma and Tony Pan and Saltz, \{Joel H.\} and Foran, \{David J.\}",

year = "2012",

doi = "10.1007/978-3-642-28145-7\_7",

language = "English",

isbn = "9783642281440",

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

number = "PART 2",

pages = "67--77",

booktitle = "Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers",

edition = "PART 2",

note = "10th International Conference on Applied Parallel and Scientific Computing, PARA 2010 ; Conference date: 06-06-2010 Through 09-06-2010",

}

Widener, PM, Kurc, T, Chen, W, Wang, F, Yang, L, Hu, J, Kumar, V, Chu, V, Cooper, L, Kong, J, Sharma, A, Pan, T, Saltz, JH & Foran, DJ 2012, High performance computing techniques for scaling image analysis workflows. in Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7134 LNCS, pp. 67-77, 10th International Conference on Applied Parallel and Scientific Computing, PARA 2010, Reykjavik, Iceland, 06/6/10. https://doi.org/10.1007/978-3-642-28145-7_7

High performance computing techniques for scaling image analysis workflows. / Widener, Patrick M.; Kurc, Tahsin; Chen, Wenjin et al.
Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers. PART 2. ed. 2012. p. 67-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7134 LNCS, No. PART 2).

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

TY - GEN

T1 - High performance computing techniques for scaling image analysis workflows

AU - Widener, Patrick M.

AU - Kurc, Tahsin

AU - Chen, Wenjin

AU - Wang, Fusheng

AU - Yang, Lin

AU - Hu, Jun

AU - Kumar, Vijay

AU - Chu, Vicky

AU - Cooper, Lee

AU - Kong, Jun

AU - Sharma, Ashish

AU - Pan, Tony

AU - Saltz, Joel H.

AU - Foran, David J.

PY - 2012

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N2 - Biomedical images are intrinsically complex with each domain and modality often requiring specialized knowledge to accurately render diagnosis and plan treatment. A general software framework that provides access to high-performance resources can make possible high-throughput investigations of micro-scale features as well as algorithm design, development and evaluation. In this paper we describe the requirements and challenges of supporting microscopy analyses of large datasets of high-resolution biomedical images. We present high-performance computing approaches for storage and retrieval of image data, image processing, and management of analysis results for additional explorations. Lastly, we describe issues surrounding the use of high performance computing for scaling image analysis workflows.

AB - Biomedical images are intrinsically complex with each domain and modality often requiring specialized knowledge to accurately render diagnosis and plan treatment. A general software framework that provides access to high-performance resources can make possible high-throughput investigations of micro-scale features as well as algorithm design, development and evaluation. In this paper we describe the requirements and challenges of supporting microscopy analyses of large datasets of high-resolution biomedical images. We present high-performance computing approaches for storage and retrieval of image data, image processing, and management of analysis results for additional explorations. Lastly, we describe issues surrounding the use of high performance computing for scaling image analysis workflows.

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

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BT - Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers

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Widener PM, Kurc T, Chen W, Wang F, Yang L, Hu J et al. High performance computing techniques for scaling image analysis workflows. In Applied Parallel and Scientific Computing - 10th International Conference, PARA 2010, Revised Selected Papers. PART 2 ed. 2012. p. 67-77. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-28145-7_7

High performance computing techniques for scaling image analysis workflows

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