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Datasets:
zeahub
/
cetus-miccai-2014

Tasks:
Image Segmentation
Modalities:
3D
Size:
n<1K
Tags:
ultrasound
echocardiography
3d
cardiac
medical
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Illegal slicing argument for scalar dataspace
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2083, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 87, in _generate_tables
                  pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
                  arr = _recursive_load_arrays(dset, features[path], start, end)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 275, in _recursive_load_arrays
                  arr = _load_array(dset, path, start, end)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 242, in _load_array
                  arr = dset[start:end]
                        ~~~~^^^^^^^^^^^
                File "h5py/_objects.pyx", line 56, in h5py._objects.with_phil.wrapper
                File "h5py/_objects.pyx", line 57, in h5py._objects.with_phil.wrapper
                File "/usr/local/lib/python3.12/site-packages/h5py/_hl/dataset.py", line 879, in __getitem__
                  selection = sel2.select_read(fspace, args)
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/h5py/_hl/selections2.py", line 101, in select_read
                  return ScalarReadSelection(fspace, args)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/h5py/_hl/selections2.py", line 86, in __init__
                  raise ValueError("Illegal slicing argument for scalar dataspace")
              ValueError: Illegal slicing argument for scalar dataspace

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CETUS - 3-D Echocardiographic Ultrasound Dataset

This dataset is a zea-format (HDF5) conversion of the CETUS (MICCAI 2014) challenge data for endocardial segmentation in 3-D echocardiography.

Property Value
Modality 3-D transthoracic echocardiography
Patients 45
Time points End-diastole (ED) and end-systole (ES) per patient
Files 90 HDF5 volumes (45 patients x 2 time points)
Voxel spacing Isotropic, ~0.576 mm (varies per patient)
Segmentation Left-ventricle endocardial surface (binary)
Splits train (1-30), val (31-38), test (39-45)

Conversion

This dataset was downloaded, converted to zea format, and uploaded using the zea data converter:

python -m zea.data.convert cetus <src> <dst> --download --upload

Dataset structure 

train/
  patient01/
    patient01_ED.hdf5
    patient01_ES.hdf5
  ...
val/
  patient31/ ...
test/
  patient39/ ...

Each HDF5 file follows the zea data format and contains:

  • data/image_sc - B-mode volume in dB, shape (1, depth, height, width)
  • non_standard_elements/segmentation - binary LV mask, same shape
  • non_standard_elements/voxel_spacing - (x, y, z) in metres
  • non_standard_elements/patient_id, time_point, citation, license

License

CC BY-NC-SA 4.0 - https://creativecommons.org/licenses/by-nc-sa-4.0/legalcode

The CETUS dataset is available free of charge strictly for non-commercial scientific research purposes only.

Citation

If you use this dataset, please cite the original CETUS paper:

@article{{bernard2016standardized,
  title   = {{Standardized Evaluation System for Left Ventricular Segmentation
              Algorithms in 3D Echocardiography}},
  author  = {{Bernard, Olivier and Bosch, Johan G. and Heyde, Brecht and
              "; Alessandrini, Martino and Barbosa, Daniel and Camarasu-Pop,
              Sorina and Cervenansky, Fr{{\'e}}d{{\'e}}ric and Valette,
              S{{\'e}}bastien and Mirea, Oana and Berber, Merih and others}},
  journal = {{IEEE Transactions on Medical Imaging}},
  volume  = {{35}},
  number  = {{4}},
  pages   = {{967--977}},
  year    = {{2016}},
  doi     = {{10.1109/tmi.2015.2503890}}
}}

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