RANDOM FORESTS ON HIERARCHICAL MULTI-SCALE SUPERVOXELS FOR LIVER TUMOR SEGMENTATION IN DYNAMIC CONTRAST-ENHANCED CT SCANS

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.