README.md

metadata

title: MONAI WholeBody CT Segmentation
emoji: 🏥
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.2.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - medical-imaging
  - segmentation
  - ct-scan
  - monai
  - 3d-segmentation
short_description: 'Whole‑body CT segmentation demo using MONAI’s SegResNet '

🏥 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

Category	Structures
Major Organs	Liver, Spleen, Kidneys, Pancreas, Gallbladder, Stomach, Bladder
Cardiovascular	Heart (4 chambers), Aorta, Vena Cava, Portal Vein, Iliac vessels
Respiratory	Lung lobes (5), Trachea, Esophagus
Skeletal	Vertebrae (C1-L5), 24 Ribs, Hip bones, Femur, Humerus, Scapula, Clavicle
Muscles	Gluteal muscles, Iliopsoas, Autochthon
Other	Brain, Face, Adrenal glands, Small/Large bowel

Usage

1. Upload a CT scan in NIfTI format (.nii or .nii.gz)
2. Click Run Segmentation and wait for processing (1-5 minutes)
3. Explore the results using the slice sliders and view controls
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}
}