Random Forests on Hierarchical Multi-Scale Supervoxels for Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans.

Pierre-Henri Conze (1) , Vincent Noblet (1) , François Rousseau (2, 3) , Fabrice Heitz (1) , Riccardo Memeo (4) , Patrick Pessaux (4)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 ITI - Département Image et Traitement Information
3 LaTIM - Laboratoire de Traitement de l'Information Medicale
4 IHU Strasbourg - L'Institut hospitalo-universitaire de Strasbourg

This paper addresses multi-label tissue classification in the context of liver tumor segmentation for patients with hepato-cellular 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 super-voxels, 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.

Mots clés: liver tumor segmentation; random forest; supervoxels; hierarchical multi-scale tree; spatial adaptivity

Domaines: Traitement des images [eess.IV]

Pierre-Henri Conze, Vincent Noblet, François Rousseau, Fabrice Heitz, Riccardo Memeo, et al.. Random Forests on Hierarchical Multi-Scale Supervoxels for Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans.. International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republic. ⟨hal-01284631⟩