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SwinUNETR

Trained by Margerie Huet Dastarac .
Training date: November 2023 .

1. Task Description

Segmentation of the body on the CT scan on a dataset 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

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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
    • RandShiftIntensityd, offsets:0.1, prob:1.0

3. Dataset

  • Location: Head and neck, oropharynx
  • Training set size: 60
  • Resolution in mm: 3x3x3

Performance

  • TBD