Eidolon: Visualization and computational framework for multi-modal biomedical data analysis

Eric Kerfoot; Lauren Fovargue; Simone Rivolo; Wenzhe Shi; Daniel Rueckert; David Nordsletten; Jack Lee; Radomir Chabiniok; Reza Razavi

doi:10.1007/978-3-319-43775-0_39

Eidolon: Visualization and computational framework for multi-modal biomedical data analysis

Eric Kerfoot, Lauren Fovargue, Simone Rivolo, Wenzhe Shi, Daniel Rueckert, David Nordsletten, Jack Lee, Radomir Chabiniok, Reza Razavi

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

19 Scopus citations

Abstract

Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing, and quantitative analysis. Medical imaging provides rich information, from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis, showing the Eidolon supports and enhances the workflow.

Original language	English (US)
Title of host publication	Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings
Editors	Hongen Liao, Guoyan Zheng, Su-Lin Lee, Philippe Cattin, Pierre Jannin
Publisher	Springer Verlag
Pages	425-437
Number of pages	13
ISBN (Print)	9783319437743
DOIs	https://doi.org/10.1007/978-3-319-43775-0_39
State	Published - 2016
Externally published	Yes
Event	7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 - Bern, Switzerland Duration: Aug 24 2016 → Aug 26 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9805 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016
Country/Territory	Switzerland
City	Bern
Period	8/24/16 → 8/26/16

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-43775-0_39

Cite this

Kerfoot, E., Fovargue, L., Rivolo, S., Shi, W., Rueckert, D., Nordsletten, D., Lee, J., Chabiniok, R., & Razavi, R. (2016). Eidolon: Visualization and computational framework for multi-modal biomedical data analysis. In H. Liao, G. Zheng, S.-L. Lee, P. Cattin, & P. Jannin (Eds.), Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings (pp. 425-437). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9805 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-43775-0_39

Eidolon: Visualization and computational framework for multi-modal biomedical data analysis. / Kerfoot, Eric; Fovargue, Lauren; Rivolo, Simone et al.
Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. ed. / Hongen Liao; Guoyan Zheng; Su-Lin Lee; Philippe Cattin; Pierre Jannin. Springer Verlag, 2016. p. 425-437 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9805 LNCS).

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

Kerfoot, E, Fovargue, L, Rivolo, S, Shi, W, Rueckert, D, Nordsletten, D, Lee, J, Chabiniok, R & Razavi, R 2016, Eidolon: Visualization and computational framework for multi-modal biomedical data analysis. in H Liao, G Zheng, S-L Lee, P Cattin & P Jannin (eds), Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9805 LNCS, Springer Verlag, pp. 425-437, 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016, Bern, Switzerland, 8/24/16. https://doi.org/10.1007/978-3-319-43775-0_39

Kerfoot E, Fovargue L, Rivolo S, Shi W, Rueckert D, Nordsletten D et al. Eidolon: Visualization and computational framework for multi-modal biomedical data analysis. In Liao H, Zheng G, Lee SL, Cattin P, Jannin P, editors, Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Springer Verlag. 2016. p. 425-437. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-43775-0_39

Kerfoot, Eric ; Fovargue, Lauren ; Rivolo, Simone et al. / Eidolon : Visualization and computational framework for multi-modal biomedical data analysis. Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. editor / Hongen Liao ; Guoyan Zheng ; Su-Lin Lee ; Philippe Cattin ; Pierre Jannin. Springer Verlag, 2016. pp. 425-437 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{01f8325e65dc4d22b17488ac31d483da,

title = "Eidolon: Visualization and computational framework for multi-modal biomedical data analysis",

abstract = "Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing, and quantitative analysis. Medical imaging provides rich information, from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis, showing the Eidolon supports and enhances the workflow.",

author = "Eric Kerfoot and Lauren Fovargue and Simone Rivolo and Wenzhe Shi and Daniel Rueckert and David Nordsletten and Jack Lee and Radomir Chabiniok and Reza Razavi",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 ; Conference date: 24-08-2016 Through 26-08-2016",

year = "2016",

doi = "10.1007/978-3-319-43775-0_39",

language = "English (US)",

isbn = "9783319437743",

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

publisher = "Springer Verlag",

pages = "425--437",

editor = "Hongen Liao and Guoyan Zheng and Su-Lin Lee and Philippe Cattin and Pierre Jannin",

booktitle = "Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings",

}

TY - GEN

T1 - Eidolon

T2 - 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016

AU - Kerfoot, Eric

AU - Fovargue, Lauren

AU - Rivolo, Simone

AU - Shi, Wenzhe

AU - Rueckert, Daniel

AU - Nordsletten, David

AU - Lee, Jack

AU - Chabiniok, Radomir

AU - Razavi, Reza

PY - 2016

Y1 - 2016

N2 - Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing, and quantitative analysis. Medical imaging provides rich information, from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis, showing the Eidolon supports and enhances the workflow.

AB - Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing, and quantitative analysis. Medical imaging provides rich information, from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis, showing the Eidolon supports and enhances the workflow.

UR - http://www.scopus.com/inward/record.url?scp=84984799856&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84984799856&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-43775-0_39

DO - 10.1007/978-3-319-43775-0_39

M3 - Conference contribution

AN - SCOPUS:84984799856

SN - 9783319437743

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

SP - 425

EP - 437

BT - Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings

A2 - Liao, Hongen

A2 - Zheng, Guoyan

A2 - Lee, Su-Lin

A2 - Cattin, Philippe

A2 - Jannin, Pierre

PB - Springer Verlag

Y2 - 24 August 2016 through 26 August 2016

ER -

Eidolon: Visualization and computational framework for multi-modal biomedical data analysis

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this