Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels

Abstract : Pre-operative locoregional treatments (PLT) delay the tumor progression by necrosis for patients with hepato-cellular carcinoma (HCC). Toward an efficient evaluation of PLT response, we address the estimation of liver tumor necrosis (TN) from CT scans. The TN rate could shortly supplant standard criteria (RECIST, mRECIST, EASL or WHO) since it has recently shown higher correlation to survival rates. To overcome the inter-expert variability induced by visual qualitative assessment, we propose a semi-automatic method that requires weak interaction efforts to segment parenchyma, tumoral active and necrotic tissues. By combining SLIC supervoxels and random decision forest, it involves discriminative multi-phase cluster-wise features extracted from registered dynamic contrast-enhanced CT scans. Quantitative assessment on expert groundtruth annotations confirms the benefits of exploiting multi-phase information from semantic regions to accurately segment HCC liver tumors.
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Pierre-Henri Conze, François Rousseau, Vincent Noblet, Fabrice Heitz, Riccardo Memeo, et al.. Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels. 6th International Workshop on Machine Learning in Medical Imaging (MLMI 2015), Oct 2015, Munich, Germany. pp.212-219, ⟨10.1007/978-3-319-24888-2_26⟩. ⟨hal-01214060⟩

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