Keywords:
- 3D Network
- 3D U-Net Author Keywords Segmentation
- algorithms
- Annotation Process
- Annotations
- Benchmark testing
- Challenge Participants
- Common Benchmark
- computed tomography MeSH Terms Cone-Beam Computed Tomography
- Cone-beam Computed Tomography Images
- Cone-beam Computed Tomography Volume
- Data Augmentation
- Deep Learning
- Deep Neural Network
- Dice Loss
- Dice Similarity Coefficient
- Domain Dataset
- EEE Keywords Three-dimensional displays
- Final Ranking
- Focal Loss
- High-resolution lung CT
- Humans
- image segmentation
- Imaging
- Inferior Alveolar Canal
- Inferior Alveolar Nerve
- Intersection Over Union
- interstitial lung diseases
- Irrigation
- Lung image analysis
- lung tissue classification
- Mandible
- Mandibular Canal
- Medical Experts
- Medical image analysis and retrieval
- Mental Foramen
- Neural Network
- Panoramic Radiographs
- Private Dataset
- Proposals Index Terms Inferior Alveolar
- Public Datasets
- semi-supervised learning
- Sparse Labeling
- Statistical Shape Model
- Surgery
- Teeth
- texture classification
- Three-Dimensional
- tooth
- training
- Training Data
- Training Set
- wavelets
- X-ray imaging
Publications of Bram van Ginneken
2025
| , , , , , , and , Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge (2025), in: IEEE Transactions on Medical Imaging, Volume 44, Issue : 4, Arpil 2025(1890-1906) |
[DOI] [URL] |
2024
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Metrics reloaded: recommendations for image analysis validation (2024), in: Nature Methods |
[DOI] |
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Understanding metric-related pitfalls in image analysis validation (2024), in: Nature Method |
[DOI] |
2021
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Common limitations of performance metrics in biomedical image analysis, in: MIDLconference, 2021 |
|
2018
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , How can we do better? Pitfalls in biomedical challenge design and how to address them, in: MICCAI workshop LABELS, 2018 |
|
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions (2018), in: arXiv |
|
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Why rankings of biomedical image analysis competitions should be interpreted with care (2018), in: Nature Communications
|
|
2008
| , , and , Lung Tissue Analysis Using Isotropic Polyharmonic B-Spline Wavelets, in: MICCAI 2008 Workshop on Pulmonary Image Analysis, pages 125-134, 2008 |
|
