#  I can't segment the model

**URL:** <https://discourse.slicer.org/t/i-cant-segment-the-model/31289>\
**Category:** Support\
**Created:** [August 22, 2023, 2:25pm UTC](https://discourse.slicer.org/t/i-cant-segment-the-model/31289 "2023-08-22T14:25:19Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![JOSE\_LUIS\_CORRAL\_PUM](https://sea2.discourse-cdn.com/flex002/user_avatar/discourse.slicer.org/jose_luis_corral_pum/32/67251_2.png) [@JOSE\_LUIS\_CORRAL\_PUM](https://discourse.slicer.org/u/JOSE_LUIS_CORRAL_PUM)\
**Post date:** [August 22, 2023, 2:25pm UTC](https://discourse.slicer.org/t/i-cant-segment-the-model/31289/1 "2023-08-22T14:25:19Z")

</div>

Operating system: Windows 10  
Procesador AMD Ryzen 5 7600X 6-Core Processor 4.70 GHz  
RAM instalada 32,0 GB (31,1 GB usable)  
Tipo de sistema Sistema operativo de 64 bits, procesador basado en x64

Slicer version:5.4.0 r31938 / 311cb26

Expected behavior: Total segmentation  
Actual behavior: Nothing segments me after the process.  
I need help please

This text appears in the console  
Processing started

Writing input file to C:/Users/azken/AppData/Local/Temp/Slicer/\_\_SlicerTemp\_\_2023-08-22\_10+45+43.576/total-segmentator-input.nii

Creating segmentations with TotalSegmentator AI…

Total Segmentator arguments: [‘-i’, ‘C:/Users/azken/AppData/Local/Temp/Slicer/\_\_SlicerTemp\_\_2023-08-22\_10+45+43.576/total-segmentator-input.nii’, ‘-o’, ‘C:/Users/azken/AppData/Local/Temp/Slicer/\_\_SlicerTemp\_\_2023-08-22\_10+45+43.576/segmentation’, ‘–ml’, ‘–task’, ‘total’]

C:\Users\azken\AppData\Local\slicer.org\Slicer 5.4.0\lib\Python\Scripts\TotalSegmentator:5: DeprecationWarning: pkg\_resources is deprecated as an API. See [Package Discovery and Resource Access using pkg\_resources - setuptools 68.1.2.post20230818 documentation](https://setuptools.pypa.io/en/latest/pkg_resources.html)

from pkg\_resources import require

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

preprocessing C:\Users\azken\AppData\Local\Temp\nnunet\_tmp\_bjfhqiz5\s01.nii.gz

using preprocessor GenericPreprocessor

before crop: (1, 109, 77, 77) after crop: (1, 109, 77, 77) spacing: [1.5 1.5 1.5]

no resampling necessary

no resampling necessary

before: {‘spacing’: array([1.5, 1.5, 1.5]), ‘spacing\_transposed’: array([1.5, 1.5, 1.5]), ‘data.shape (data is transposed)’: (1, 109, 77, 77)}

after: {‘spacing’: array([1.5, 1.5, 1.5]), ‘data.shape (data is resampled)’: (1, 109, 77, 77)}

(1, 109, 77, 77)

This worker has ended successfully, no errors to report

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

force\_separate\_z: None interpolation order: 0

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

preprocessing C:\Users\azken\AppData\Local\Temp\nnunet\_tmp\_bjfhqiz5\s01.nii.gz

using preprocessor GenericPreprocessor

before crop: (1, 109, 77, 77) after crop: (1, 109, 77, 77) spacing: [1.5 1.5 1.5]

no resampling necessary

no resampling necessary

before: {‘spacing’: array([1.5, 1.5, 1.5]), ‘spacing\_transposed’: array([1.5, 1.5, 1.5]), ‘data.shape (data is transposed)’: (1, 109, 77, 77)}

after: {‘spacing’: array([1.5, 1.5, 1.5]), ‘data.shape (data is resampled)’: (1, 109, 77, 77)}

(1, 109, 77, 77)

This worker has ended successfully, no errors to report

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

force\_separate\_z: None interpolation order: 0

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

preprocessing C:\Users\azken\AppData\Local\Temp\nnunet\_tmp\_bjfhqiz5\s01.nii.gz

using preprocessor GenericPreprocessor

before crop: (1, 109, 77, 77) after crop: (1, 109, 77, 77) spacing: [1.5 1.5 1.5]

no resampling necessary

no resampling necessary

before: {‘spacing’: array([1.5, 1.5, 1.5]), ‘spacing\_transposed’: array([1.5, 1.5, 1.5]), ‘data.shape (data is transposed)’: (1, 109, 77, 77)}

after: {‘spacing’: array([1.5, 1.5, 1.5]), ‘data.shape (data is resampled)’: (1, 109, 77, 77)}

(1, 109, 77, 77)

This worker has ended successfully, no errors to report

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

force\_separate\_z: None interpolation order: 0

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

preprocessing C:\Users\azken\AppData\Local\Temp\nnunet\_tmp\_bjfhqiz5\s01.nii.gz

using preprocessor GenericPreprocessor

before crop: (1, 109, 77, 77) after crop: (1, 109, 77, 77) spacing: [1.5 1.5 1.5]

no resampling necessary

no resampling necessary

before: {‘spacing’: array([1.5, 1.5, 1.5]), ‘spacing\_transposed’: array([1.5, 1.5, 1.5]), ‘data.shape (data is transposed)’: (1, 109, 77, 77)}

after: {‘spacing’: array([1.5, 1.5, 1.5]), ‘data.shape (data is resampled)’: (1, 109, 77, 77)}

(1, 109, 77, 77)

This worker has ended successfully, no errors to report

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

force\_separate\_z: None interpolation order: 0

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

preprocessing C:\Users\azken\AppData\Local\Temp\nnunet\_tmp\_bjfhqiz5\s01.nii.gz

using preprocessor GenericPreprocessor

before crop: (1, 109, 77, 77) after crop: (1, 109, 77, 77) spacing: [1.5 1.5 1.5]

no resampling necessary

no resampling necessary

before: {‘spacing’: array([1.5, 1.5, 1.5]), ‘spacing\_transposed’: array([1.5, 1.5, 1.5]), ‘data.shape (data is transposed)’: (1, 109, 77, 77)}

after: {‘spacing’: array([1.5, 1.5, 1.5]), ‘data.shape (data is resampled)’: (1, 109, 77, 77)}

(1, 109, 77, 77)

This worker has ended successfully, no errors to report

Please cite the following paper when using nnUNet:

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.” Nat Methods (2020). [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | Nature Methods](https://doi.org/10.1038/s41592-020-01008-z)

If you have questions or suggestions, feel free to open an issue at [GitHub - MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet)

force\_separate\_z: None interpolation order: 0

If you use this tool please cite: [[2208.05868] TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://doi.org/10.48550/arXiv.2208.05868)

Resampling…

Resampled in 0.92s

Predicting part 1 of 5 …

Predicting part 2 of 5 …

Predicting part 3 of 5 …

Predicting part 4 of 5 …

Predicting part 5 of 5 …

Predicted in 21.36s

Resampling…

Saving segmentations…

Saved in 0.17s

Importing segmentation results…

Cleaning up temporary folder…

Processing completed in 26.22 seconds

Processing finished.