Datasets:

rjbaw
/

oct

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              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 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/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.

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Skin OCT Surface Localization Dataset

Description

This dataset contains optical coherence tomography (OCT) B-scans acquired with an in-house robot-mounted OCT/OCE imaging system for skin-surface localization.

The dataset was used to evaluate DINOCT, a robust skin-surface localizer for artifact-prone OCT images affected by structured polarization-maintaining-fiber artifacts.

Data

Each raw OCT B-scan has size 512 × 500 pixels.

Visible-surface scans include a manually annotated skin-surface centerline, represented as one depth coordinate per image column.

The dataset also includes non-visible/background frames where no valid skin surface is visible.

Splits

Train, validation, and test splits are provided at the recording/acquisition level.

Subject identifiers and detailed demographic metadata were not consistently recorded, so the splits should not be interpreted as guaranteed subject-wise splits.

Intended Use

This dataset is intended for research on:

OCT skin-surface localization
OCT boundary detection
artifact robustness
robot-mounted OCT/OCE image guidance

Not Intended For

This dataset is not intended for:

clinical diagnosis
treatment planning
autonomous medical decision-making
demographic or subgroup analysis
subject identification or re-identification

Human Subjects and Privacy

Human-subject imaging was approved by the University of Washington Institutional Review Board, and written informed consent was obtained from all participants.

Released files do not include direct subject identifiers.

Do not attempt to identify participants or link this dataset with external personal data.

Limitations

All data were acquired on a single in-house OCT/OCE platform. Subject metadata, anatomical site metadata, and formal inter-annotator variability measurements are not included. The real-artifact stress set should be interpreted as an artifact-stress evaluation set rather than a population-level out-of-distribution benchmark.

Citation

Please cite the associated paper if you use this dataset:

@article{dinoct_2026,
  title   = {DINOCT: robust skin-surface localization in OCT under structured PM-fiber artifacts},
  author  = {Bawornkitchaikul, Raveeroj and Pelivanov, Ivan and O'Donnell, Matt},
  year    = {2026}
}

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