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livermask

Automatic liver parenchyma and vessel segmentation in CT using deep learning

livermask was developed by SINTEF Medical Technology to provide an open tool to accelerate research.

An online version of the tool has been made openly available at Hugging Face spaces, to enable researchers to easily test the software on their own data without downloading it. To access it, click on the badge above.

A stable release is available on PyPI:

pip install livermask

Alternatively, to install from source do:

pip install git+https://github.com/andreped/livermask.git

As TensorFlow 2.4 only supports Python 3.6-3.8, so does livermask. Softwareis also compatible with Anaconda. However, best way of installing livermask is usingpip, whichalso works for conda environments.

(Optional) To add GPU inference support for liver vessel segmentation (which uses Chainer and CuPy), you need to installCuPy. This can be easily done by addingcupy-cudaX, whereX is the CUDA version you have installed, for instancecupy-cuda110 for CUDA-11.0:

pip install cupy-cuda110

Program has been tested using Python 3.7 on Windows, macOS, and Ubuntu Linux 20.04.

livermask --input path-to-input --output path-to-output

command	description
--input	the full path to the input data. Could be nifti file or directory (if directory is provided as input)
--output	the full path to the output data. Could be either output name or directory (if directory is provided as input)
--cpu	to disable the GPU (force computations on CPU only)
--verbose	to enable verbose
--vessels	to segment vessels
--extension	which extension to save output in (default:.nii)

python -m livermask.livermask --input path-to-input --output path-to-output

dcm2niix -s y -m y -d 1 "path_to_CT_folder" "output_name"

pip install --force-reinstall --no-deps git+https://github.com/andreped/livermask.git

pip install 'cupy-cuda110>=7.7.0,<8.0.0'

• Wang et al., Machine learning-based radiomic models for predicting metachronous liver metastases in colorectal cancer patients: a multimodal study, Research Square (preprint), 2024,https://doi.org/10.21203/rs.3.rs-3320033/v1
• Yevdokimov et al., Recognition of Diffuse Hepatic Steatosis, 33rd Conference of Open Innovations Association, FRUCT, 2023,https://doi.org/10.23919/FRUCT58615.2023.10143062
• Pérez de Frutos et al., Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation, PLOS ONE, 2023,https://doi.org/10.1371/journal.pone.0282110
• Lee et al., Robust End-to-End Focal Liver Lesion Detection Using Unregistered Multiphase Computed Tomography Images, IEEE Transactions on Emerging Topics in Computational Intelligence, 2021,https://doi.org/10.1109/TETCI.2021.3132382
• Survarachakan et al., Effects of Enhancement on Deep Learning Based Hepatic Vessel Segmentation, Electronics, 2021,https://doi.org/10.3390/electronics10101165

The segmentation models were evaluated on an internal dataset against manual annotations. See Table E in S4 Appendix in the Supporting Information ofthis paper for more information. The table presented there can also be seen below:

The parenchyma segmentation model was trained on the LITS dataset, whereas the vessel model was trained on a local dataset (Oslo-CoMet). The LITS dataset is openly accessible and can be downloaded fromhere.

The Oslo-CoMet included 60 patients, of which 11 representative patients were used as hold out sample for the performance metrics assessment.

If you found this tool helpful in your research, please, consider citing it (seehere for more information on how to cite):

@software{andre_pedersen_2023_7574587,  author       = {André Pedersen and Javier Pérez de Frutos},  title        = {andreped/livermask: v1.4.1},  month        = jan,  year         = 2023,  publisher    = {Zenodo},  version      = {v1.4.1},  doi          = {10.5281/zenodo.7574587},  url          = {https://doi.org/10.5281/zenodo.7574587}}

In addition, the segmentation performance of the tool was presented inthis paper, thus, cite this tool as well if that is of relevance for you study:

@article{perezdefrutos2022ddmr,    title = {Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation},    author = {Pérez de Frutos, Javier AND Pedersen, André AND Pelanis, Egidijus AND Bouget, David AND Survarachakan, Shanmugapriya AND Langø, Thomas AND Elle, Ole-Jakob AND Lindseth, Frank},    journal = {PLOS ONE},    publisher = {Public Library of Science},    year = {2023},    month = {02},    volume = {18},    doi = {10.1371/journal.pone.0282110},    url = {https://doi.org/10.1371/journal.pone.0282110},    pages = {1-14},    number = {2}}