VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation
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Summary
A DL-based label fusion strategy (VoteNet) which locally selects a set of reliable atlases whose labels are then fused via plurality voting is proposed which significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach.
- Type
- article
- Published
- 2019-04-18
- Cited by
- 123
- References
- 16
- OpenAlex
- https://openalex.org/W36108312
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:126148497
Keywords
Humanities, Art
References
- Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach
- Multi-Atlas Segmentation of Biomedical Images: A Survey
- Fully convolutional networks for semantic segmentation
- Efficient classifier generation and weighted voting for atlas-based segmentation: two small steps faster and closer to the combination oracle
- Automatic anatomical brain MRI segmentation combining label propagation and decision fusion
- Learning to Rank Atlases for Multiple-Atlas Segmentation
- A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR Images
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- Neural Network Ensembles
- Iterative Multi-Atlas-Based Multi-Image Segmentation with Tree-Based Registration
- Multi-Atlas Segmentation with Joint Label Fusion
- Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation
- Neighbourhood approximation using randomized forests
- Neural multi‐atlas label fusion: Application to cardiac MR images
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- Multi-atlas Segmentation with Learning-Based Label Fusion
Cited by
- VOTENET+ : AN IMPROVED DEEP LEARNING LABEL FUSION METHOD FOR MULTI-ATLAS SEGMENTATION
- Robustness study of noisy annotation in deep learning based medical image segmentation
- LT-Net: Label Transfer by Learning Reversible Voxel-Wise Correspondence for One-Shot Medical Image Segmentation
- Fully Automated Segmentation of 2D and 3D Mobile Mapping Data for Reliable Modeling of Surface Structures Using Deep Learning
- Local Temperature Scaling for Probability Calibration
- Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
- LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks
- RoIFusion: 3D Object Detection From LiDAR and Vision
- Deep Learning for 3D Point Cloud Understanding: A Survey
- VOTENET++: REGISTRATION REFINEMENT FOR MULTI-ATLAS SEGMENTATION
- EfficientLPS: Efficient LiDAR Panoptic Segmentation
- DiTNet: End-to-End 3D Object Detection and Track ID Assignment in Spatio-Temporal World
- ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection
- Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation
- TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding
- Semi-Supervised 3d Object Detection Via Adaptive Pseudo-Labeling
- SOMA: Subject-, object-, and modality-adapted precision atlas approach for automatic anatomy recognition and delineation in medical images
- SI-RCNN: A Shape-Invariant Set-Abstraction for 3D Object detection
- A semi-supervised 3D object detection method for autonomous driving
- Spatial Attention Frustum: A 3D Object Detection Method Focusing on Occluded Objects
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