Introducing TotalSegmentator: An Advanced Tool for Medical Image Segmentation
TotalSegmentator is a sophisticated tool designed for segmenting major anatomical structures in CT and MR images. With its training based on a diverse set of images from various scanners and institutions, it offers robust performance across different imaging protocols.
Overview and Capabilities
TotalSegmentator can effectively segment anatomical features such as organs, vessels, bones, and muscles in both CT and MRI scans. This versatility is thanks to its comprehensive training dataset, which is publicly accessible for those interested in exploring it further. The tool supports various tasks, offering segmentation maps for a wide array of anatomical structures.
Main categories of anatomical segmentation include, but are not limited to:
- CT Main Classes: Comprehensive segmentation of anatomical structures.
- MR Main Classes: Specialized segmentation tasks for MR imaging.
For detailed class information, users can view available structures through the TotalSegmentator interface.
Development and Citation
Developed by the Research and Analysis Department at the University Hospital Basel, TotalSegmentator builds upon the nnUNet framework. It has been recognized in academic literature, and users of this tool are encouraged to cite its foundational papers, especially when used for professional or research purposes.
Installation and Usage
TotalSegmentator is compatible with Ubuntu, Mac, and Windows, and supports both CPU and GPU computation. Installing TotalSegmentator requires Python 3.9 or higher and Pytorch between versions 2.0.0 and 2.4 (for Windows users).
To Install:
pip install TotalSegmentator
Basic Usage:
For CT image segmentation:
TotalSegmentator -i ct.nii.gz -o segmentations
For MR image segmentation:
TotalSegmentator -i mri.nii.gz -o segmentations --task total_mr
Note: Input images can be in Nifti format or as a folder containing all DICOM slices for a patient.
Subtasks and Specialized Features
TotalSegmentator provides a range of subtasks beyond the default segmentation, tailored for specific structures and can be employed for both CT and MR images:
- Lung Vessels
- Body Segmentation
- Cerebral Hemorrhage Detection
- Heart Chambers Analysis (available with additional licensing)
Each task is designed to cater to specific clinical or research needs, sharpening focus on particular anatomical components.
Advanced Usage and Customization
Advanced users can leverage options for enhanced efficiency or specific application needs:
- Use
--fastfor quicker processing with lower resolution. - Focus on specific regions using
--roi_subset. - Generate 3D previews with the
--previewoption. - Comprehensive statistical output with
--statisticsor--radiomics.
Running TotalSegmentator in Docker
For convenience and ease of integration into existing workflows, TotalSegmentator is also available as a Docker container:
docker run --gpus 'device=0' --ipc=host -v /path/to/data:/tmp wasserth/totalsegmentator:2.2.1 TotalSegmentator -i /tmp/ct.nii.gz -o /tmp/segmentations
Resource Requirements and Optimization
Running TotalSegmentator efficiently requires informed decisions about resource allocation, especially when segmenting large datasets. Users can leverage options such as --fast or --roi_subset to mitigate memory demands.
Integration and Support
TotalSegmentator supports a Python API for seamless integration into custom software solutions, offering flexibility in script-based applications.
Community and Feedback
The developers of TotalSegmentator encourage users to provide feedback and suggest classes to include in future updates. This continuous interaction aims to refine the tool and align its capabilities with evolving clinical and research needs.
Conclusion
TotalSegmentator represents a significant advancement in anatomical segmentation of imaging data, offering a robust and reliable solution for researchers and clinicians alike. Its adaptability to different imaging modalities and its extensive features make it a valuable asset in the field of medical imaging analysis. For more information and resources, users are encouraged to explore the documentation and get hands-on experience through installation and use.