tamias-zenodo-liver-models

TAMIAS mirror of the Zenodo liver-segmentation models (ONNX)

ONNX exports (opset 17, fp32, fixed patch) and TAMIAS manifests converted from:

• LightningMedSeg3D trained weights: 10.5281/zenodo.21037952. The weights are CC-BY-4.0, see LICENSE-zenodo-21037952. The architecture code, LightningMedSeg3D, is AGPL-3.0-or-later.
• nnU-Net v2 Dataset006_Liver (liver + lesion, LiTS 2017, from BAMF Health): 10.5281/zenodo.11582728. CC-BY-4.0. nnU-Net is Apache-2.0.

Conversion code: scripts/zenodo/ in ArioMoniri/semikap. The GitHub release is zenodo-models-v1. zenodo-models-index.json (schema tamias.model-index.v1) lists every file with its sha256, labels, parity and licence.

Input contract

• Input tensor: float32 [1,1,Z,Y,X] after reorienting to manifest.orientation (RAS) and resampling to manifest.spacing.
• Output tensor: logits [1,C,Z,Y,X].
• LightningMedSeg3D models: the manifest applies a window normalisation (level 37.5, width 425 HU).
• nnU-Net model: CTNormalization is baked into the graph, so the manifest uses none.

Parity check: PyTorch runs the reference pipeline and onnxruntime runs the TAMIAS pipeline. Both run on a random-HU patch and a synthetic CT phantom.

Research use only. Not a medical device. Do not use for clinical decisions.

Citation

@dataset{fdezgonzalez_lightningmedseg3d_weights_2026,
  author    = {Fdez-Gonz{\'a}lez, Marcos and Nodar-Corral, Lois and Fdez-Vidal, Xose R. and
               Est{\'e}vez-Fern{\'a}ndez, Sergio and Comesa{\~n}a, Enrique},
  title     = {Trained Weights of Nine 3D Medical Image Segmentation Networks on {BTCV} and
               {MSD} {Task03} ({Liver}) --- {LightningMedSeg3D}},
  year      = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.21037952}
}
@software{lightningmedseg3d,
  author = {Fdez-Gonz{\'a}lez, Marcos and Removirt},
  title  = {{LightningMedSeg3D}: A {PyTorch} {Lightning} framework for 3D medical image segmentation},
  url    = {https://github.com/Removirt/LightningMedSeg3D}
}
@software{murugesan_liver_nnunet_2024,
  author    = {Murugesan, Gowtham Krishnan and Van Oss, Jeff and McCrumb, Diana},
  title     = {Pretrained model for 3D semantic image segmentation of the liver and liver lesions from ct scan},
  year      = {2024}, publisher = {Zenodo}, doi = {10.5281/zenodo.11582728}
}
@article{isensee2021nnunet,
  author  = {Isensee, F. and Jaeger, P. F. and Kohl, S. A. and Petersen, J. and Maier-Hein, K. H.},
  title   = {nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  journal = {Nature Methods}, volume = {18}, number = {2}, pages = {203--211}, year = {2021}
}
@article{bilic2023lits,
  author  = {Bilic, P. and Christ, P. and Li, H. B. and others},
  title   = {The Liver Tumor Segmentation Benchmark ({LiTS})},
  journal = {Medical Image Analysis}, volume = {84}, pages = {102680}, year = {2023}
}