ArticleInternational journal of computer assisted radiology and surgery2017
Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans.
Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multi-Scale Convolutional Attention and Structural Re-Parameterized Residual-Based 3D U-Net for Liver and Liver Tumor Segmentation from CT.Sensors (Basel, Switzerland) · 2025Article
- DGA-Net: a dual-branch group aggregation network for liver tumor segmentation in medical images.Frontiers in medical technology · 2025Article
- A Comparative Study of Decoders for Liver and Tumor Segmentation Using a Self-ONN-Based Cascaded Framework.Diagnostics (Basel, Switzerland) · 2024Article
- The Liver Tumor Segmentation Benchmark (LiTS).Medical image analysis · 2023Article
- Regional Gas Transport During Conventional and Oscillatory Ventilation Assessed by Xenon-Enhanced Computed Tomography.Annals of biomedical engineering · 2021Article
- State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2021Review
- Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation.Frontiers in oncology · 2021Article
- SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging.European radiology · 2020Article
- Quantifying Regional Lung Deformation Using Four-Dimensional Computed Tomography: A Comparison of Conventional and Oscillatory Ventilation.Frontiers in physiology · 2020Article
- Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.International journal of computer assisted radiology and surgery · 2019Article
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7 authors.
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Abstract
purposeToward an efficient clinical management of hepatocellular carcinoma (HCC), we propose a classification framework dedicated to tumor necrosis rate estimation from dynamic contrast-enhanced CT scans. Based on machine learning, it requires weak interaction efforts to segment healthy, active and necrotic liver tissues.
methodsOur contributions are two-fold. First, we apply random forest (RF) on supervoxels using multi-phase supervoxel-based features that discriminate tissues based on their dynamic in response to contrast agent injection. Second, we extend this technique in a hierarchical multi-scale fashion to deal with multiple spatial extents and appearance heterogeneity. It translates in an adaptive data sampling scheme combining RF and hierarchical multi-scale tree resulting from recursive supervoxel decomposition. By concatenating multi-phase features across the hierarchical multi-scale tree to describe leaf supervoxels, we enable RF to automatically infer the most informative scales without defining any explicit rules on how to combine them.
resultsAssessment on clinical data confirms the benefits of multi-phase information embedded in a multi-scale supervoxel representation for HCC tumor segmentation.
conclusionDedicated but not limited only to HCC management, both contributions reach further steps toward more accurate multi-label tissue classification.
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