Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study
Saved in:
| Published in: | PLOS ONE |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study
- AI-Driven Deep Learning for Lung Segmentation in CT Imaging
- VoxelMorph-Based Non-Rigid Chest CT Registration With Multi-Frequency Encoding and Curriculum Learning
- Broadening of Spectral Lines for Decaying Trapped States of Photonic Meta‐Atoms
- Towards digital photon counting cameras for single-molecule optical nanoscopy
- A Case Study about Counting Uncertainty of Radioactive Iodine (131I) in Public Waters by Using ...
- Panoptic segmentation for complete labeling of fruit microstructure in 3D micro-CT images with deep learning