Unbiased Stratification of Left Ventricles
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
| Hi-index | 0.02 |
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 interand intra-cluster stratification as assessed by ANOSIM R was 0.887 ± 0.18 (pval