Paper Info
Title
Random Forests On Hierarchical Multi-Scale Supervoxels For Liver Tumor Segmentation In Dynamic Contrast-Enhanced Ct Scans
Abstract
This paper addresses multi-label tissue classification in the context of liver tumor segmentation for patients with hepatocellular carcinoma (HCC). Covering such issue in an interactive perspective through supervoxel-based random forest (RF) requires an adaptive data sampling scheme to deal with multiple spatial extents and appearance heterogeneity. We propose a simple and efficient strategy combining standard RF and hierarchical multi-scale tree resulting from recursive 3D SLIC supervoxel decomposition. By concatenating features across the hierarchical multi-scale tree to describe leaf supervoxels, we enable RF to automatically infer the most informative scales discriminating tissues based on their intrinsic properties. Our method does not require any explicit rules on how to combine the different scales. Quantitative assessment on expert ground truth annotations demonstrates improved results compared to standard single-scale strategies for HCC tumor segmentation in dynamic contrast-enhanced CT scans.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493296
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Keywords
Field
DocType
liver tumor segmentation, random forest, supervoxels, hierarchical multi-scale tree, spatial adaptivity
Data mining,Pattern recognition,Computer science,Dynamic Contrast Enhanced CT,Ground truth,Concatenation,Artificial intelligence,Quantitative assessment,Data sampling,Random forest,Liver tumor segmentation,Recursion
Conference
ISSN
Citations 
PageRank 
1945-7928
2
0.37
References 
Authors
8
6
Name
Order
Citations
PageRank
Pierre-Henri Conze1337.73
Vincent Noblet291.59
Francois Rousseau312116.81
Fabrice Heitz440159.55
Riccardo Memeo591.25
Patrick Pessaux6102.22