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
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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
If you have questions or suggestions, feel free to open an issue at GitHub - 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
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.