Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24888-2_26 | MLMI |
Field | DocType | Citations |
Tumor progression,Liver tumor,Pattern recognition,Computer science,Dynamic Contrast Enhanced CT,Artificial intelligence,Quantitative assessment,Radiology,Random forest,Discriminative model,Liver tumor segmentation,Carcinoma | Conference | 2 |
PageRank | References | Authors |
0.40 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre-Henri Conze | 1 | 33 | 7.73 |
| Francois Rousseau | 2 | 121 | 16.81 |
| Vincent Noblet | 3 | 9 | 1.59 |
| Fabrice Heitz | 4 | 401 | 59.55 |
| Riccardo Memeo | 5 | 9 | 1.25 |
| Patrick Pessaux | 6 | 10 | 2.22 |