TY - GEN
T1 - Referenceless stratification of parenchymal lung abnormalities
AU - Raghunath, Sushravya
AU - Rajagopalan, Srinivasan
AU - Karwoski, Ronald A.
AU - Bartholmai, Brian J.
AU - Robb, Richard A.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - This paper introduces computational tools that could enable personalized, predictive, preemptive, and participatory (P4) Pulmonary medicine. We demonstrate approaches to (a) stratify lungs from different subjects based on the spatial distribution of parenchymal abnormality and (b) visualize the stratification through glyphs that convey both the grouping efficacy and an iconic overview of an individual's lung wellness. Affinity propagation based on regional parenchymal abnormalities is used in the referenceless stratification. Abnormalities are computed using supervised classification based on Earth Mover's distance. Twenty natural clusters were detected from 372 CT lung scans. The computed clusters correlated with clinical consensus of 9 disease types. The quality of inter- and intra-cluster stratification as assessed by ANOSIM R was 0.887 ± 0.18 (pval < 0.0005). The proposed tools could serve as biomarkers to objectively diagnose pathology, track progression and assess pharmacologic response within and across patients.
AB - This paper introduces computational tools that could enable personalized, predictive, preemptive, and participatory (P4) Pulmonary medicine. We demonstrate approaches to (a) stratify lungs from different subjects based on the spatial distribution of parenchymal abnormality and (b) visualize the stratification through glyphs that convey both the grouping efficacy and an iconic overview of an individual's lung wellness. Affinity propagation based on regional parenchymal abnormalities is used in the referenceless stratification. Abnormalities are computed using supervised classification based on Earth Mover's distance. Twenty natural clusters were detected from 372 CT lung scans. The computed clusters correlated with clinical consensus of 9 disease types. The quality of inter- and intra-cluster stratification as assessed by ANOSIM R was 0.887 ± 0.18 (pval < 0.0005). The proposed tools could serve as biomarkers to objectively diagnose pathology, track progression and assess pharmacologic response within and across patients.
KW - Referenceless stratification
KW - affinity propagation
KW - glyphs
KW - idiopathic pulmonary fibrosis
UR - http://www.scopus.com/inward/record.url?scp=82255164561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82255164561&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23626-6_28
DO - 10.1007/978-3-642-23626-6_28
M3 - Conference contribution
C2 - 22003703
AN - SCOPUS:82255164561
SN - 9783642236259
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 223
EP - 230
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 22 September 2011
ER -