Automatic brain labeling via multi‐atlas guided fully convolutional networks☆
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Summary
The proposed multi‐atlas guided fully convolutional network (MA‐FCN) aims at further improving the labeling performance with the aid of prior knowledge from the training atlases, by significantly outperforming the conventional FCN and several state‐of‐the‐art MR brain labeling methods.
- Type
- article
- Published
- 2019-01-01
- Cited by
- 32
- References
- 64
- Access
- Open access
- OpenAlex
- https://openalex.org/W30447544
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53944116
Keywords
Nuclear engineering, Nitrogen, Materials science, Environmental science, Physics
References
- Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains
- An Optimized PatchMatch for multi-scale and multi-feature label fusion
- Multi-Atlas Segmentation of Biomedical Images: A Survey
- A transversal approach for patch-based label fusion via matrix completion
- Brain tumor segmentation with Deep Neural Networks
- Fully convolutional networks for semantic segmentation
- Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- A generative probability model of joint label fusion for multi-atlas based brain segmentation
- Automatic hippocampus segmentation of 7.0 Tesla MR images by combining multiple atlases and auto-context models
- Altered Cerebellar Functional Connectivity with Intrinsic Connectivity Networks in Adults with Major Depressive Disorder
- HAMMER: hierarchical attribute matching mechanism for elastic registration
- Advances in functional and structural MR image analysis and implementation as FSL.
- Sex differences in the structural connectome of the human brain
- Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
- Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling
- Structural Growth Trajectories and Rates of Change in the First 3 Months of Infant Brain Development
- Encoding atlases by randomized classification forests for efficient multi-atlas label propagation
- Learning to Rank Atlases for Multiple-Atlas Segmentation
- LEAP: Learning embeddings for atlas propagation
Cited by
- Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis
- Multi-Atlas Brain Parcellation Using Squeeze-and-Excitation Fully Convolutional Networks
- Computational tools for objective assessment in Neuroimaging
- Automated Morphological Measurements of Brain Structures and Identification of Optimal Surgical Intervention for Chiari I Malformation
- Balanced multi-image demons for non-rigid registration of magnetic resonance images.
- Brain Image Parcellation Using Fully Convolutional Network with Adaptively Selected Features from Brain Atlases
- Dual Attention Multi-Instance Deep Learning for Alzheimer’s Disease Diagnosis With Structural MRI
- Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model
- Whole Brain Segmentation with Full Volume Neural Network
- Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy
- Modified FCN-based Semantic Segmentation Algorithm for Empty Area Recognition in Paddy
- Integrated 3d flow-based multi-atlas brain structure segmentation
- A Machine Learning Approach to Support Treatment Identification for Chiari I Malformation
- LabelScr: Automated Image Annotation on Mobile and Web Applications for Object Detection and Localization Model Pipelines
- An end-to-end infant brain parcellation pipeline
- Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasound
- Controversies and progress on standardization of large-scale brain network nomenclature
- DSANet: Dual-Branch Shape-Aware Network for Echocardiography Segmentation in Apical Views
- A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond
- Image Annotation Optimization Method of Civil Engineering Supervision Based on Association Rule Mining Algorithm
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