IFMedTech/CT_Segmentation

🏥 MONAI WholeBody CT Segmentation

Automatic 3D segmentation of 104 anatomical structures from CT scans

Overview

This application uses MONAI's pre-trained SegResNet model trained on the TotalSegmentator dataset to automatically segment 104 different anatomical structures from whole-body CT scans.

Features

• —🔬 104 Anatomical Structures: Segments organs, bones, muscles, and vessels
• —📊 Interactive Visualization: Navigate through axial, coronal, and sagittal views
• —🎨 Color-coded Overlay: Each structure has a distinct color for easy identification
• —⚡ GPU Accelerated: Uses CUDA when available for faster inference

Supported Structures

Usage

1. 1.Upload a CT scan in NIfTI format (.nii or .nii.gz)
2. 2.Click Run Segmentation and wait for processing (1-5 minutes)
3. 3.Explore the results using the slice sliders and view controls
4. 4.Check the Detected Structures panel to see all identified anatomy

Model Details

• —Architecture: SegResNet (MONAI)
• —Resolution: 3.0mm isotropic (low-resolution model)
• —Training Data: TotalSegmentator dataset
• —Output: 105 channels (background + 104 structures)

References

• —MONAI Model Zoo
• —TotalSegmentator Paper
• —TotalSegmentator GitHub

License

This model is released under the Apache 2.0 License. The TotalSegmentator dataset is released under CC BY 4.0.

Citation

If you use this model, please cite:

@article{wasserthal2023totalsegmentator,
  title={TotalSegmentator: robust segmentation of 104 anatomical structures in CT images},
  author={Wasserthal, Jakob and others},
  journal={Radiology: Artificial Intelligence},
  year={2023}
}