metadata
'[object Object]': null
language:
- en
SwinUNETR
Trained by Margerie Huet Dastarac
Training date: November2023
1. Task Description
Segmentation of the body on the CT scan on a datasheet of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.
2. Model
2.1. Architecture
Figure 1: SwinUNETR architecture
2.2. Input
- CT
2.3. Output
- BODY
2.4 Training details
- Number of epoch: 300
- Loss function: Dice loss
- Optimizer: Adam
- Learning Rate: 3e-4
- Dropout: No
- Patch size in voxels: (128,128,128)
- Data augmentation used:
- RandSpatialCropd
- RandFlipd axis=0
- RandFlipd axis=1
- RandFlipd axis=2
- NormalizeIntensityd
- RandScaleIntensityd factors=0.1 prob=1.0
3. Dataset
- Location: Head and neck, oropharynx
- Training set size: 60
- Data type: CT scan and body contours
- Resolution in mm: 3x3x3
- Preprocessing
Performance
+TBD