CT-Segmentation is a lightweight and practical medical imaging segmentation GUI built using PyQt5.
It provides an easy end-to-end workflow to:
- ✅ Load a DICOM folder
- ✅ Convert it into a NIfTI (.nii.gz) volume
- ✅ Run automated segmentation using modern AI tools
This project is useful for researchers, students, and developers working on CT imaging pipelines, helping reduce the manual effort of preparing data and running segmentation from the command line.
It supports:
- Skellytour ( bone segmentation)
- TotalSegmentator (bone and organ segmentation tasks)
- Python 3.10
- PyQt5
- SimpleITK
- Skellytour (install before running this GUI)
- TotalSegmentator (install before running this GUI)
conda create -y -n ctseg python=3.10
conda activate ctsegpip install PyQt5 SimpleITKgit clone https://github.com/cpwardell/Skellytour.git
cd Skellytour/
python -m pip install .Verify Skellytour:
skellytour --helppip install totalsegmentatorVerify TotalSegmentator:
TotalSegmentator --helpRun the GUI
python app.py-
Click Select DICOM Folder
-
Click Select Output Folder
-
Click Convert DICOM → NIfTI
Creates: ct.nii.gz inside the output folder
-
Select Segmentation Method
Skellytour
Model: low / medium / high
Device: gpu / cpu
TotalSegmentator
Select a task from the dropdown
Click Run Segmentation
<output_folder>/ct.nii.gzSaved inside:
<output_folder>/Saved inside:
<output_folder>/segmentations_<task_name>/- Make sure skellytour and TotalSegmentator commands work in your terminal before using the GUI.
- GPU is recommended for faster segmentation, but CPU also works.
- TotalSegmentator may require a license depending on your usage:
- A non-commercial license is available.
- For commercial usage, please check the official TotalSegmentator license terms.