RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
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Summary
This work proposes a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver.
- Type
- article
- Published
- 2018-11-04
- Cited by
- 442
- References
- 66
- Access
- Open access
- OpenAlex
- https://openalex.org/W2899279931
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53220603
Keywords
Computer science, Segmentation, Residual, Artificial intelligence, Deep learning
References
- Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
- A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes
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- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
- World Cancer Report
- Fully convolutional networks for semantic segmentation
- Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
- 3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms
- Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation
- Support vector machine classification and validation of cancer tissue samples using microarray expression data
- Towards a deep learning approach to brain parcellation
- Attention to Scale: Scale-Aware Semantic Image Segmentation
- Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes
- Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks
- Deep Residual Learning for Image Recognition
- Sub-cortical brain structure segmentation using F-CNN'S
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
- Brain Tumor Segmentation
Cited by
- Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation
- Probability Map Guided Bi-directional Recurrent UNet for Pancreas Segmentation
- Brain Tumor Segmentation on MRI with Missing Modalities
- Automatic segmentation of kidney and liver tumors in CT images
- Attention-Based DenseUnet Network With Adversarial Training for Skin Lesion Segmentation
- Integration of a knowledge-based constraint into generative models with applications in semi-automatic segmentation of liver tumors
- BE-FNet: 3D Bounding Box Estimation Feature Pyramid Network for Accurate and Efficient Maxillary Sinus Segmentation
- Automated Left Ventricular Myocardium Segmentation Using 3D Deeply Supervised Attention U-Net for Coronary Computed Tomography Angiography.
- CU-Net: Cascaded U-Net Model for Automated Liver and Lesion Segmentation and Summarization
- Multi-level Glioma Segmentation using 3D U-Net Combined Attention Mechanism with Atrous Convolution
- Coronary Artery Segmentation from Intravascular Optical Coherence Tomography Using Deep Capsules
- AResU-Net: Attention Residual U-Net for Brain Tumor Segmentation
- A review of recent progress in deep learning-based methods for MRI brain tumor segmentation
- Hybrid Cascaded Neural Network for Liver Lesion Segmentation
- 3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics
- Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis
- Hybrid Segmentation Algorithm for Medical Image Segmentation Based on Generating Adversarial Networks, Mutual Information and Multi-Scale Information
- U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images
- Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images
- Deep Learning for Automated Liver Segmentation to Aid in the Study of infectious Diseases in Nonhuman Primates
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