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Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
Volumetric image segmentation with convolutional neural networks (CNNs) ...
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Feature Sensitive Label Fusion with Random Walker for Atlas-based Image Segmentation
In this paper, a novel label fusion method is proposed for brain magneti...
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Learning Deformable Registration of Medical Images with Anatomical Constraints
Deformable image registration is a fundamental problem in the field of m...
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Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
The interpretation of medical images is a challenging task, often compli...
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Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
Segmentation is a fundamental task for extracting semantically meaningfu...
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Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a ch...
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An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
Automatic segmentation methods are important tools for quantitative anal...
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VoteNet++: Registration Refinement for Multi-Atlas Segmentation
Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors before label fusion. Specifically, we use a volumetric displacement field to refine registrations based on image anatomical appearance and predicted labels. We show the influence of the initial spatial alignment as well as the beneficial effect of using label information for MAS performance. Experiments demonstrate that the proposed refinement approach improves MAS performance on a 3D magnetic resonance dataset of the knee.
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