Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans

Conze, Pierre-Henri; Noblet, Vincent; Rousseau, François; Heitz, Fabrice; Blasi, Vito De; Méméo, Riccardo; Pessaux, Patrick

doi:10.1007/s11548-016-1493-1

Cited by 53 publications

(41 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 Octree decomposition, a three-dimensional extension of quadtree decomposition, was used to partition each ROI into eight octants according to bisecting coronal, sagittal, and transverse planes (Perchiazzi et al, 2014). Supervoxel decomposition was used to partition each ROI into two new ROIs by a weighted k-means clustering of voxels according to intensity similarity and spatial proximity (Conze et al, 2017). Recursive subdivision was continued until all ROIs either contained belowthreshold values of standard deviation, or were <0.2% of the lung mask.…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

Quantifying Regional Lung Deformation Using Four-Dimensional Computed Tomography: A Comparison of Conventional and Oscillatory Ventilation

et al. 2020

View full text Add to dashboard Cite

Mechanical ventilation strategies that reduce the heterogeneity of regional lung stress and strain may reduce the risk of ventilator-induced lung injury (VILI). In this study, we used registration of four-dimensional computed tomographic (4DCT) images to assess regional lung aeration and deformation in 10 pigs under baseline conditions and following acute lung injury induced with oleic acid. CT images were obtained via dynamic axial imaging (Siemens SOMATOM Force) during conventional pressure-controlled mechanical ventilation (CMV), as well as high-frequency and multi-frequency oscillatory ventilation modalities (HFOV and MFOV, respectively). Our results demonstrate that oscillatory modalities reduce intratidal strain throughout the lung in comparison to conventional ventilation, as well as the spatial gradients of dynamic strain along the dorsal-ventral axis. Harmonic distortion of parenchymal deformation was observed during HFOV with a single discrete sinusoid delivered at the airway opening, suggesting inherent mechanical nonlinearity of the lung tissues. MFOV may therefore provide improved lung-protective ventilation by reducing strain magnitudes and spatial gradients of strain compared to either CMV or HFOV.

show abstract

Section: Image Acquisition and Processingmentioning

confidence: 99%

Quantifying Regional Lung Deformation Using Four-Dimensional Computed Tomography: A Comparison of Conventional and Oscillatory Ventilation

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This describes the sequence of regions that are used to form the region and gives a multi-scale representation of the region under consideration. This idea [9] has been initially used to represent a leaf node by a sequence of its ascendants. In the proposed approach, this idea is applied in a bottom-up way to suit our approach and keep the same outcome.…”

Section: Contour-constrained Slic (Coslic)mentioning

confidence: 99%

Adaptive strategy for superpixel-based region-growing image segmentation

Chaibou

Conze

Kalti

et al. 2017

J. Electron. Imaging

Self Cite

View full text Add to dashboard Cite

International audienceThis work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained oversegmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we first introduce a robust adaptive multiscale superpixel similarity in which region comparisons are made both at content and common border level. Second, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adaptive merging criterion to ensure that best region aggregations are given highest priorities. This allows the ability to reach a final segmentation into consistent regions with strong boundary adherence. We perform experiments on the BSDS500 image dataset to highlight to which extent our method compares favorably against other well-known image segmentation algorithms. The obtained results demonstrate the promising potential of the proposed approach

show abstract

“…On the other hand, region-based features can be computed [15] for image characterization. Different strategies have shown their performance to define relevant image regions for feature extraction such as using sliding windows [16], supervoxels [17,18] or hierarchical image models (HS). Among these models, the component-tree [19] allows one to spectrally and spatially represent an image by a tree structure derived from the inclusion relation on the binary level-sets of the image.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical forest attributes for multimodal tumor segmentation on FDG-PET/contrast-enhanced CT

Padilla

Romaniuk

Naegel

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

View full text Add to dashboard Cite

Accurate tumor volume delineation is a crucial step for disease assessment, treatment planning and monitoring of several kinds of cancers. However, this process is complex due to variations in tumors properties. Recently, some methods have been proposed for taking advantage of the spatial and spectral information carried by coupled modalities (e.g., PET-CT, MRI-PET). Simultaneously, the development of attributebased approaches has contributed to improve PET image analysis. In this work, we aim at developing a coupled multimodal / attribute-based approach for image segmentation. Our proposal is to take advantage of hierarchical image models for determining relevant and specific attribute from each modality. These attributes then allow us to define a unique, semantic vectorial image. Sequentially, this image can be processed by a standard segmentation method, in our case a randomwalker approach, for segmenting tumors based on their intrinsic attribute-based properties. Experimental results emphasize the relevance of computing region-based attributes from both modalities.

show abstract

Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans

Abstract: Dedicated but not limited only to HCC management, both contributions reach further steps toward more accurate multi-label tissue classification.

Cited by 53 publications

References 31 publications

Quantifying Regional Lung Deformation Using Four-Dimensional Computed Tomography: A Comparison of Conventional and Oscillatory Ventilation

Quantifying Regional Lung Deformation Using Four-Dimensional Computed Tomography: A Comparison of Conventional and Oscillatory Ventilation

Adaptive strategy for superpixel-based region-growing image segmentation

Hierarchical forest attributes for multimodal tumor segmentation on FDG-PET/contrast-enhanced CT

Contact Info

Product

Resources

About