Omnidirectional Volume Slicer - OmniSlicer

Implementation of the omnidirectional volume slicer package in Python and PyTorch for 3D medical image analysis, from our Medical Image Analysis publication "TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks".

Example Usage

from OmniSlicer import OmniSlicer

volume_path = "path_to_volume.nii.gz"
mask_path = "path_to_mask.nii.gz"
output_dir = "output_dir"
n_views = N

OmniSlicer.extract_slices(volume_path=volume_path,
                          mask_path=mask_path,
                          output_dir=output_dir,
                          n_views=n_views)

Tested Dependencies

The functionality of OmniSlicer has been successfully validated using the following dependency versions. These represent the environment in which the package has been developed and tested:

These versions are defined in the project’s installation requirements and are automatically resolved when installing OmniSlicer via pip. While other combinations may work, the dependency set above is the configuration against which all core features have been verified. Please make sure that you install torch with CUDA.

Citation

@misc{kiechle2025tomographview3dmedicalimage,
      title={TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks}, 
      author={Johannes Kiechle and Stefan M. Fischer and Daniel M. Lang and Cosmin I. Bercea and Matthew J. Nyflot and Lina Felsner and Julia A. Schnabel and Jan C. Peeken},
      year={2025},
      eprint={2511.09605},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2511.09605}, 
}