# nnunetv1
**Note: Unit tests have not been added to this repository yet.**
### Summary
Run inference by the nnUnet model [1, 2] on an input NIfTI file.
Testing datasets, from http://medicaldecathlon.com/ [3]:
- Task04_Hippocampus.tar (a 3-D dateset)
- Task05_Prostate.tar (a 4-D dataset)
### Cite
[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. (2020). nnU-Net: a
self-configuring method for deep learning-based biomedical image segmentation.
Nat Methods. https://doi.org/10.1038/s41592-020-01008-z.
[2] Isensee, F., Jäger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein,
K. H. (2021). pretrained models for 3D semantic image segmentation with nnU-Net
(2.1). Zenodo. https://doi.org/10.5281/zenodo.4485926.
[3] Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K.,
Van Ginneken, B., ... & Cardoso, M. J. (2019). A large annotated medical image
dataset for the development and evaluation of segmentation algorithms.
arXiv preprint arXiv:1902.09063.
### License
The nnUnet source code is under an [Apache License 2.0](https://github.com/MIC-DKFZ/nnUNet/blob/nnunetv1/LICENSE); the remainder of this gear is under [MIT License](https://gitlab.com/flywheel-io/scientific-solutions/gears/templates/skeleton/-/blob/76f2c943ad5212380e39b27a756574bce94a3782/LICENSE).
### Classification
*Category:* *Analysis*
*Gear Level:*
- [x] Project
- [x] Subject
- [x] Session
- [x] Acquisition
- [x] Analysis
----
[[_TOC_]]
----
### Inputs
- __modality_0__
- __Name__: modality_0
- __Type__: .nii.gz
- __Optional__: false
- __Classification__: *file*
- __Description__: *3D or 4D NIfTI file.*
- __Notes__: *If 4D, should be only modality input.*
- __modality_1__
- __Name__: modality_1
- __Type__: .nii.gz
- __Optional__: true
- __Classification__: *file*
- __Description__: *3D NIfTI file.*
- __Notes__: *If this exists, all other inputs should also be 3D.*
- __modality_2__
- __Name__: modality_2
- __Type__: .nii.gz
- __Optional__: true
- __Classification__: *file*
- __Description__: *3D NIfTI file.*
- __Notes__: *If this exists, all other inputs should also be 3D.*
- __modality_3__
- __Name__: modality_3
- __Type__: .nii.gz
- __Optional__: true
- __Classification__: *file*
- __Description__: *3D NIfTI file.*
- __Notes__: *If this exists, all other inputs should also be 3D.*
### Config
- __pretrained_model__
- __type__: *str*
- __Description__: *Pre-trained model to use for inference*. 10 options given for Task models 00-10 [2].
- __debug__
- __type__: *bool*
- __Description__: Whether to include debug statements in the job logs.
### Output Files
- __prediction_time.txt__
- __postprocessing.json__
- __plans.pkl__
- __{input NIfTI file base name}\_\_{model name}.nii.gz__
### Workflow
1. Upload file(s) to container.
2. Select file(s) as input(s) to gear.
3. Specify which model to apply inference with, as well as any other config
selections and run.
3. Gear runs inference with selected model on inputs and places results into
into new Analysis container.
## Contributing
[For more information about how to get started contributing to that gear,
checkout [CONTRIBUTING.md](CONTRIBUTING.md).]
<!-- markdownlint-disable-file -->
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"description": "# nnunetv1\n\n**Note: Unit tests have not been added to this repository yet.**\n\n\n### Summary\n\nRun inference by the nnUnet model [1, 2] on an input NIfTI file.\n\nTesting datasets, from http://medicaldecathlon.com/ [3]:\n- Task04_Hippocampus.tar (a 3-D dateset)\n- Task05_Prostate.tar (a 4-D dataset)\n\n\n### Cite\n\n[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. (2020). nnU-Net: a\nself-configuring method for deep learning-based biomedical image segmentation.\nNat Methods. https://doi.org/10.1038/s41592-020-01008-z.\n\n[2] Isensee, F., J\u00e4ger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein,\nK. H. (2021). pretrained models for 3D semantic image segmentation with nnU-Net\n(2.1). Zenodo. https://doi.org/10.5281/zenodo.4485926.\n\n[3] Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K.,\nVan Ginneken, B., ... & Cardoso, M. J. (2019). A large annotated medical image\ndataset for the development and evaluation of segmentation algorithms.\narXiv preprint arXiv:1902.09063.\n\n\n### License\n\nThe nnUnet source code is under an [Apache License 2.0](https://github.com/MIC-DKFZ/nnUNet/blob/nnunetv1/LICENSE); the remainder of this gear is under [MIT License](https://gitlab.com/flywheel-io/scientific-solutions/gears/templates/skeleton/-/blob/76f2c943ad5212380e39b27a756574bce94a3782/LICENSE).\n\n\n### Classification\n\n*Category:* *Analysis*\n\n*Gear Level:*\n\n- [x] Project\n- [x] Subject\n- [x] Session\n- [x] Acquisition\n- [x] Analysis\n\n----\n\n[[_TOC_]]\n\n----\n\n\n### Inputs\n\n- __modality_0__\n - __Name__: modality_0\n - __Type__: .nii.gz\n - __Optional__: false\n - __Classification__: *file*\n - __Description__: *3D or 4D NIfTI file.*\n - __Notes__: *If 4D, should be only modality input.*\n\n- __modality_1__\n - __Name__: modality_1\n - __Type__: .nii.gz\n - __Optional__: true\n - __Classification__: *file*\n - __Description__: *3D NIfTI file.*\n - __Notes__: *If this exists, all other inputs should also be 3D.*\n\n- __modality_2__\n - __Name__: modality_2\n - __Type__: .nii.gz\n - __Optional__: true\n - __Classification__: *file*\n - __Description__: *3D NIfTI file.*\n - __Notes__: *If this exists, all other inputs should also be 3D.*\n\n- __modality_3__\n - __Name__: modality_3\n - __Type__: .nii.gz\n - __Optional__: true\n - __Classification__: *file*\n - __Description__: *3D NIfTI file.*\n - __Notes__: *If this exists, all other inputs should also be 3D.*\n\n\n### Config\n\n- __pretrained_model__\n - __type__: *str*\n - __Description__: *Pre-trained model to use for inference*. 10 options given for Task models 00-10 [2].\n\n- __debug__\n - __type__: *bool*\n - __Description__: Whether to include debug statements in the job logs.\n\n\n### Output Files\n\n - __prediction_time.txt__\n - __postprocessing.json__\n - __plans.pkl__\n - __{input NIfTI file base name}\\_\\_{model name}.nii.gz__\n\n\n### Workflow\n\n1. Upload file(s) to container.\n2. Select file(s) as input(s) to gear.\n3. Specify which model to apply inference with, as well as any other config\nselections and run.\n3. Gear runs inference with selected model on inputs and places results into\ninto new Analysis container.\n\n\n## Contributing\n\n[For more information about how to get started contributing to that gear,\ncheckout [CONTRIBUTING.md](CONTRIBUTING.md).]\n<!-- markdownlint-disable-file -->",
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