Description
Trained semantic segmentation models, data description and performance metrics supporting the conference paper "Trained nnU-Net model for semantic segmentation of human adult cervical vertebrae from CT-Scans". No medical images or labelmaps are provided in this repository.
When using our trained models, we would ask you to cite our paper Diotalevi et al. (submitted) and both papers from Isensee et al. (2021 & 2024) as we rely on their model architecture.
If you require access to medical images and labelmaps, please refer to the cited datasets and download them or contact their administrators. Cite them accordingly, if you use their datasets.
Python, 3.11
nnU-Net library, 2
When using our trained models, we would ask you to cite our paper Diotalevi et al. (submitted) and both papers from Isensee et al. (2021 & 2024) as we rely on their model architecture.
If you require access to medical images and labelmaps, please refer to the cited datasets and download them or contact their administrators. Cite them accordingly, if you use their datasets.
Python, 3.11
nnU-Net library, 2
| Date made available | 7 Feb 2025 |
|---|---|
| Publisher | Borealis |
| Geographical coverage | Montreal, Quebec, CANADA |
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