TY - GEN
T1 - Imaging as a surrogate for the early prediction and assessment of treatment response through the analysis of 4-D texture ensembles (ISEPARATE)
AU - Maday, Peter
AU - Khurd, Parmeshwar
AU - Ladic, Lance
AU - Schnall, Mitchell
AU - Rosen, Mark
AU - Davatzikos, Christos
AU - Kamen, Ali
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - image registration
KW - texture classification
KW - therapy response
UR - http://www.scopus.com/inward/record.url?scp=79951606605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951606605&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-18421-5_16
DO - 10.1007/978-3-642-18421-5_16
M3 - Conference contribution
AN - SCOPUS:79951606605
SN - 9783642184208
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 173
BT - Medical Computer Vision
T2 - 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
Y2 - 20 September 2010 through 20 September 2010
ER -