Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE)

Peter Maday; Parmeshwar Khurd; Lance Ladic; Mitchell Schnall; Mark Rosen; Christos Davatzikos; Ali Kamen

doi:10.1007/978-3-642-18421-5_16

Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE)

Peter Maday, Parmeshwar Khurd, Lance Ladic, Mitchell Schnall, Mark Rosen, Christos Davatzikos, Ali Kamen

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

3 Scopus citations

Abstract

In order to facilitate the use of imaging as a surrogate endpoint for the early prediction and assessment of treatment response, we present a quantitative image analysis system to process the anatomical and functional images acquired over the course of treatment. The key features of our system are deformable registration, texture analysis via texton histograms, feature selection using the minimal-redundancy-maximal-relevance method, and classification using support vector machines. The objective of the proposed image analysis and machine learning methods in our system is to permit the identification of multi-parametric imaging phenotypic properties that have superior diagnostic and prognostic value as compared to currently used morphometric measurements. We evaluate our system for predicting treatment response of breast cancer patients undergoing neoadjuvant chemotherapy using a series of MRI acquisitions.

Original language	English (US)
Title of host publication	Medical Computer Vision
Subtitle of host publication	Recognition Techniques and Applications in Medical Imaging - International MICCAI Workshop, MCV 2010, Revised Selected Papers
Pages	164-173
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-18421-5_16
State	Published - 2011
Externally published	Yes
Event	Workshop on Medical Computer Vision, MCV 2010, Held in Conjunction with the 13th International Conference on Medical Image Computing and Computer - Assisted Intervention, MICCAI 2010 - Beijing, China Duration: Sep 20 2010 → Sep 20 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6533 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	Workshop on Medical Computer Vision, MCV 2010, Held in Conjunction with the 13th International Conference on Medical Image Computing and Computer - Assisted Intervention, MICCAI 2010
Country/Territory	China
City	Beijing
Period	9/20/10 → 9/20/10

Keywords

image registration
texture classification
therapy response

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-18421-5_16

Cite this

Maday, P., Khurd, P., Ladic, L., Schnall, M., Rosen, M., Davatzikos, C., & Kamen, A. (2011). Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE). In Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging - International MICCAI Workshop, MCV 2010, Revised Selected Papers (pp. 164-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6533 LNCS). https://doi.org/10.1007/978-3-642-18421-5_16

Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE). / Maday, Peter; Khurd, Parmeshwar; Ladic, Lance et al.
Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging - International MICCAI Workshop, MCV 2010, Revised Selected Papers. 2011. p. 164-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6533 LNCS).

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

Maday, P, Khurd, P, Ladic, L, Schnall, M, Rosen, M, Davatzikos, C & Kamen, A 2011, Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE). in Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging - International MICCAI Workshop, MCV 2010, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6533 LNCS, pp. 164-173, Workshop on Medical Computer Vision, MCV 2010, Held in Conjunction with the 13th International Conference on Medical Image Computing and Computer - Assisted Intervention, MICCAI 2010, Beijing, China, 9/20/10. https://doi.org/10.1007/978-3-642-18421-5_16

Maday P, Khurd P, Ladic L, Schnall M, Rosen M, Davatzikos C et al. Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE). In Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging - International MICCAI Workshop, MCV 2010, Revised Selected Papers. 2011. p. 164-173. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-18421-5_16

Maday, Peter ; Khurd, Parmeshwar ; Ladic, Lance et al. / Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE). Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging - International MICCAI Workshop, MCV 2010, Revised Selected Papers. 2011. pp. 164-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AU - Rosen, Mark

AU - Davatzikos, Christos

AU - Kamen, Ali

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Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE)

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Keywords

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