https://github.com/reubendo/tumorsegmentator

https://github.com/reubendo/tumorsegmentator

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/reubendo/tumorsegmentator
• Owner: ReubenDo
• License: mit
• Created: 2024-03-26T22:40:14.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-03-27T22:15:31.000Z (9 months ago)
• Last Synced: 2024-03-28T20:24:09.561Z (9 months ago)
• Language: Python
• Size: 29.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# TumorSegmentator

### Installation

TumorSegmentator works on Ubuntu, Mac, and Windows and on CPU and GPU.

Install dependencies:

* Python >= 3.9

* [Pytorch](http://pytorch.org/) >= 2.0.0

Install TumorSegmentator

```

pip install -U git+https://github.com/ReubenDo/TumorSegmentator#egg=TumorSegmentator

```

### Usage

```

TumorSegmentator -t1 t1.nii.gz -cet1 cet1.nii.gz -o segmentations

TumorSegmentator -t1 t1.nii.gz -cet1 cet1.nii.gz -t2 t2.nii.gz -flair flair.nii.gz -o segmentations

```

> Note: A Nifti file or a folder with all DICOM slices of one patient is allowed as input

> Note: This is not a medical device and is not intended for clinical usage.

### Python API

You can run TumorSegmentator via Python:

```python

import nibabel as nib

from tumorsegmentator.python_api import tumorsegmentator

if __name__ == "__main__":

    # option 1: provide input and output as file paths

    tumorsegmentator(input_path_t1, input_path_cet1, input_path_t2, input_path_flair, output_path)

    # option 2: provide input and output as nifti image objects

    input_img = nib.load(input_path)

    output_img = tumorsegmentator(input_img)

    nib.save(output_img, output_path)

```

You can see all available arguments [here](https://github.com/reubendo/TumorSegmentator/blob/master/TumorSegmentator/python_api.py). Running from within the main environment should avoid some multiprocessing issues.

The segmentation image contains the names of the classes in the extended header. If you want to load this additional header information you can use the following code:

```python

from tumorsegmentator.nifti_ext_header import load_multilabel_nifti

segmentation_nifti_img, label_map_dict = load_multilabel_nifti(image_path)

```

The above code requires `pip install xmltodict`.

### Install latest master branch (contains latest bug fixes)

```

pip install git+https://github.com/reubendo/TumorSegmentator.git

```

### Typical problems

**ITK loading Error**

When you get the following error message

```

ITK ERROR: ITK only supports orthonormal direction cosines. No orthonormal definition was found!

```

you should do

```

pip install SimpleITK==2.0.2

```

Alternatively you can try

```

fslorient -copysform2qform input_file

fslreorient2std input_file output_file

```

### Reference

TODO