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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 missing columns ({'knee_tibia_right', 'knee_tibia_left'})

This happened while the json dataset builder was generating data using

hf://datasets/westfechtel/paper-augmentation/torsion_format/ankle/mild/Participant_14_glutes_lf/values_baseline.json (at revision 72abf74d7cf92cb62bdc3065d15aa214d983f256)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              hip_right: double
              hip_left: double
              knee_femur_right: double
              knee_femur_left: double
              femur_right: double
              femur_left: double
              ankle_right: double
              ankle_left: double
              tibia_right: double
              tibia_left: double
              to
              {'hip_right': Value(dtype='float64', id=None), 'hip_left': Value(dtype='float64', id=None), 'knee_femur_right': Value(dtype='float64', id=None), 'knee_femur_left': Value(dtype='float64', id=None), 'femur_right': Value(dtype='float64', id=None), 'femur_left': Value(dtype='float64', id=None), 'knee_tibia_right': Value(dtype='float64', id=None), 'knee_tibia_left': Value(dtype='float64', id=None), 'ankle_right': Value(dtype='float64', id=None), 'ankle_left': Value(dtype='float64', id=None), 'tibia_right': Value(dtype='float64', id=None), 'tibia_left': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1391, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 990, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 missing columns ({'knee_tibia_right', 'knee_tibia_left'})
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/westfechtel/paper-augmentation/torsion_format/ankle/mild/Participant_14_glutes_lf/values_baseline.json (at revision 72abf74d7cf92cb62bdc3065d15aa214d983f256)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

hip_right
float64
hip_left
float64
knee_femur_right
float64
knee_femur_left
float64
femur_right
float64
femur_left
float64
knee_tibia_right
float64
knee_tibia_left
float64
ankle_right
float64
ankle_left
float64
tibia_right
float64
tibia_left
float64
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End of preview.

Dataset structure

  • checkpoints: Contains nnunet checkpoint files for hip segmentation (proximal femur), knee segmentation (distal femur and proximal tibia) and ankle segmentation (distal tibia and distal fibula). Three checkpoint files per segmentation model, one for each augmentation scheme (baseline aka no augmentation, default aka nnunet augmentation and mr aka MRI-specific augmentation).
  • testdata_raw: Contains the test dataset in raw form, i.e. original dicom series. Per participant (n=20), there are five dicom files: one for the reference exam and four each for the motion-pattern exams (gluteal contraction in high (glutes_hf) and low (glutes_lf) frequency and plantar-/dorsiflexion in high (feet_hf) and low (feet_lf) frequency).
  • torsion_format: Contains test data structured for torsional alignment quantification post-processing. Data is organised into reference (no artefact) images, images with mild, moderate and severe artefacts. Each series directory (named participant_[exam], e.g. participant_1_feet_lf is the series resulting from the low-frequency plantar-/dorsiflexion exam for participant #1) contains:
    • in nifti format: raw images for hip, knee and ankle
    • in nifti format: segmentation masks for hip, knee and ankle (Y_seg.nii.gz = segmentation by MRI-augmented model, Y_seg_baseline = segmentation by baseline model, Y_seg_default = segmentation by default-augmented model)
    • in nifti format: segmentation masks + reference lines for hip, knee, and ankle (Y_ref_)
    • in json format: computed torsion angles. hip_left/right = angle of proximal reference line. knee_femur_left/right = angle of distal femur reference line. femur_left/right = femoral torsion angle. knee_tibia_left/right = angle of proximal tibia reference line. ankle_left/right = angle of distal tibia/fibula reference line. tibia_left/right = tibial torsion angle. If a key is missing, the algorithm failed to compute the corresponding referenc line / angle.

Code

Code is available at github.com/swestfechtel/paper-augmentation

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