The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 246, in _generate_tables
pa_table = paj.read_json(
^^^^^^^^^^^^^^
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 97, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 260, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 106, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
TCIA_CervicalCancer (CC-Tumor-Heterogeneity)
Longitudinal multi-parametric MRI + 18F-FDG PET/CT collection of locally advanced cervical cancer patients undergoing chemoradiation, with radiation-oncologist tumor contours (RTSTRUCT) defined on T2-weighted MRI as the gold standard. Each patient was imaged at three time points spanning the chemoradiation course.
Dataset Details
| Field | Value |
|---|---|
| Modalities | MR (T1W, T2W, DCE, DWI), 18F-FDG PET, CT |
| Body part | Cervix / pelvis |
| Task | 3D tumor segmentation on T2W MRI (longitudinal) |
| Patients | 23 (CCTH-A01 … CCTH-A12, CCTH-B01 … CCTH-B11) |
| Time points | 3 per patient — Baseline (0 Gy) / Early ( |
| Studies | 171 |
| Series | 821 |
| Images | 131,556 DICOM slices |
| MR series | 523 (15.91 GB) |
| CT series | 65 (11.48 GB) |
| PET series | 68 (0.99 GB) |
| RTSTRUCT files | 68 (≈ 23 patients × 3 time points; CCTH-A06 has 2) |
| REG (registration) files | 97 |
| Format | DICOM (images) + DICOM RTSTRUCT (contours) + DICOM REG (registrations) |
| License | CC BY 4.0 |
Segmentation
Tumor contours are stored as DICOM RTSTRUCT files in segmentations/.
Each RTSTRUCT references a single MR T2W sagittal series via
ReferencedFrameOfReferenceSequence → RTReferencedStudySequence → RTReferencedSeriesSequence.SeriesInstanceUID — that referenced series is the
image volume the contour is defined on. ROI names follow Ut-MRT2-Sag-{1|2|3}
where the trailing index matches the time point (1 = Baseline, 2 = Early,
3 = Mid-treatment), and StructureSetName carries Timepoint{1|2|3}.
The collection paper describes contours as tumor volume only — no OARs and no lymph nodes, drawn by the study radiation oncologists with T2W MRI chosen for its highest soft-tissue contrast for cervical tumor delineation.
Structure
images_mr/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # MR (T1W, T2W, DCE, DWI variants)
images_ct/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # CT
images_pt/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # PET
segmentations/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # RTSTRUCT (one per time point)
registrations/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # DICOM REG (PET/CT ↔ MR coregistration)
series_to_patient.json # series-level index
PatientID ranges over CCTH-A01..A12 and CCTH-B01..B11. StudyDescription
in each RTSTRUCT carries MR1 / MR2 / MR3, identifying the time point.
CCTH-B11 has no CT series; CCTH-A06 has 2 RTSTRUCTs (one time point missing).
Source
- TCIA collection: https://www.cancerimagingarchive.net/collection/cc-tumor-heterogeneity/
- DOI:
10.7937/ERZ5-QZ59 - License URL: https://creativecommons.org/licenses/by/4.0/
- Released: 2023-01-20 (now fully public, no registration required)
Citation
@misc{mayr2023ccth,
author = {Mayr, N. and Yuh, W. T. C. and Bowen, S. and Harkenrider, M. and
Knopp, M. V. and Lee, E. Y.-P. and Leung, E. and Lo, S. S. and
Small Jr., W. and Wolfson, A. H.},
title = {Cervical Cancer -- Tumor Heterogeneity: Serial Functional and
Molecular Imaging Across the Radiation Therapy Course in
Advanced Cervical Cancer (Version 1) [Data set]},
year = {2023},
publisher = {The Cancer Imaging Archive},
doi = {10.7937/ERZ5-QZ59}
}
@article{bowen2017radiomic,
author = {Bowen, S. R. and Yuh, W. T. C. and Hippe, D. S. and Wu, W. and
Partridge, S. C. and Elias, S. and Jia, G. and Huang, Z. and
Sandison, G. A. and Nelson, D. and Knopp, M. V. and Lo, S. S. and
Kinahan, P. E. and Mayr, N. A.},
title = {Tumor radiomic heterogeneity: Multiparametric functional imaging
to characterize variability and predict response following
cervical cancer radiation therapy},
journal = {Journal of Magnetic Resonance Imaging},
volume = {47},
number = {5},
pages = {1388--1396},
year = {2017},
doi = {10.1002/jmri.25874}
}
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