Paper Info
Title
Total Variation Random Forest: Fully Automatic Mri Segmentation In Congenital Heart Diseases
Abstract
This paper proposes a fully automatic supervised segmentation technique for segmenting the great vessel and blood pool of pediatric cardiac MRIs of children with Congenital Heart Defects (CHD). CHD affects the overall anatomy of heart, rendering model-based segmentation framework infeasible, unless a large dataset of annotated images is available. However, the cardiac anatomy still retains distinct appearance patterns, which has been exploited in this work. In particular, Total Variation (TV) is introduced for solving the 3D disparity and noise removal problem. This results in homogeneous appearances within anatomical structures which is exploited further in a Random Forest framework. Context-aware appearance models are learnt using Random Forest (RF) for appearance-based prediction of great vessel and blood pool of an unseen subject during testing. We have obtained promising results on the HVSMR16 training dataset in a leave-one-out cross-validation.
Year
DOI
Venue
2016
10.1007/978-3-319-52280-7_17
RECONSTRUCTION, SEGMENTATION, AND ANALYSIS OF MEDICAL IMAGES
Keywords
DocType
Volume
Total Variation, Random Forest, Congenital heart disease, 3D cardiac MRI, Automatic segmentation
Conference
10129
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Anirban Mukhopadhyay171150.07