Reza Azad, Leon Niggemeier, Michael Hüttemann, Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci and Dorit Merhof

State-of-the art comparison on the abdominal multi-organ Synapse dataset for 2D methods. For all models the model complexity and the performance (DSC, HD95) is shown. The proposed 2D D-LKA Net achieves superior segmentation performance. Abbreviations stand for: Spl: spleen, RKid: right kidney, LKid: left kidney, Gal: gallbladder, Liv: liver, Sto: stomach, Aor: aorta, Pan: pancreas. Best results are shown in blue, second best in red.

State-of-the art comparison on the abdominal multi-organ Synapse dataset for 3D methods. For all models the model complexity and the performance (DSC, HD95) is shown. The proposed 3D D-LKA Net achieves superior segmentation performance. Our models also is considerably small with the lowest number of parameters. Abbreviations stand for: Spl: spleen, RKid: right kidney, LKid: left kidney, Gal: gallbladder, Liv: liver, Sto: stomach, Aor: aorta, Pan: pancreas. Best results are shown in blue, second best in red.

While the 2D version achieves great segmentation results in comparison to other 2D models, the main limitation is the lack of inter-slice connections. Here, the 3D models achieves favorable segmentations.

For detailed instruction for the 2D methods, please refer to the Readme in the 2D folder.

For detailed instruction for the 3D methods, please refer to the Readme in the 3D folder.

This repository is built based on nnFormer, UNETR++, transnorm, MCF, D3D. We thank the authors for their code repositories.

All implementations done by Leon Niggemeier. For any query please contact us for more information.

leon.niggemeier@rwth-aachen.de

@article{azad2023beyond,
  title={Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation},
  author={Azad, Reza and Niggemeier, Leon and Huttemann, Michael and Kazerouni, Amirhossein and Aghdam, Ehsan Khodapanah and Velichko, Yury and Bagci, Ulas and Merhof, Dorit},
  journal={arXiv preprint arXiv:2309.00121},
  year={2023}
}