3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review
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Summary
The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks instead of methods with manual feature engineering, e.g., Random Regression Forests.
- Type
- preprint
- Published
- 2020-12-10
- Cited by
- 13
- References
- 67
- Access
- Open access
- OpenAlex
- https://openalex.org/W3112633583
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:228083745
Keywords
Minimum bounding box, Bounding overwatch, Computer science, Implementation, Convolutional neural network
References
- Ray-casting based evaluation framework for haptic force feedback during percutaneous transhepatic catheter drainage punctures
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- A Virtual Reality System for PTCD Simulation Using Direct Visuo-Haptic Rendering of Partially Segmented Image Data
- Patch-based label fusion using local confidence-measures and weak segmentations
- ImageNet classification with deep convolutional neural networks
- Multi-organ localization with cascaded global-to-local regression and shape prior
- Deep Residual Learning for Image Recognition
- Direct Visuo-Haptic 4D Volume Rendering Using Respiratory Motion Models
- Random forest classification of large volume structures for visuo-haptic rendering in CT images
- 2D image classification for 3D anatomy localization: employing deep convolutional neural networks
- Real-Time Ultrasound Simulation for Training of US-Guided Needle Insertion in Breathing Virtual Patients
- Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks
- Efficient patient modeling for visuo-haptic VR simulation using a generic patient atlas
- Optimized Image-Based Soft Tissue Deformation Algorithms for Visualization of Haptic Needle Insertion
- Direct Haptic Volume Rendering in Lumbar Puncture Simulation
- Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- YOLO9000: Better, Faster, Stronger
- Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆
Cited by
- Fast Lung Localization in Computed Tomography by a 1D Detection Network
- Comparison of 2D vs 3D U-Net Organ Segmentation in abdominal 3D CT images
- Neurovascular bundles segmentation on MRI via hierarchical object activation network
- CBCT lung multi-OAR segmentation via hierarchical network
- A tomographic workflow to enable deep learning for X-ray based foreign object detection
- Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip
- Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance
- Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus
- Gravity Network for end-to-end small lesion detection
- Smart Compliance: A Supervised Learning- Based Classification of Clients Dress Code for Policy Enforcement in Philippine Government Offices
- Strategies for Deep Learning in Volumetric Medical Imaging: A Survey
- Advanced 3D U-Net and Encoder-Decoder CNN Models for Brain Tumor Segmentation in MRI
- Concept for Automatic Multi-object Organ Detection and Segmentation in Abdominal CT Data
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