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Datasets:
vectorsense
/
organscan-data

Tasks:
Image Classification
Tags:
medical-imaging
anatomy
ultrasound
computed-tomography
image-classification
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:    CastError
Message:      Couldn't cast
schema_version: string
taxonomy_version: string
evidence_build: null
evidence_note: string
promotion_thresholds: struct<note: string, model_trained_min_frames: int64, model_trained_min_patients: int64, model_train (... 86 chars omitted)
  child 0, note: string
  child 1, model_trained_min_frames: int64
  child 2, model_trained_min_patients: int64
  child 3, model_trained_min_sites: int64
  child 4, experimental_min_frames: int64
  child 5, experimental_min_patients: int64
levels: struct<modality: struct<index: list<item: string>, indeterminate: string, description: string>, regi (... 168 chars omitted)
  child 0, modality: struct<index: list<item: string>, indeterminate: string, description: string>
      child 0, index: list<item: string>
          child 0, item: string
      child 1, indeterminate: string
      child 2, description: string
  child 1, region: struct<index: list<item: string>, indeterminate: string, description: string>
      child 0, index: list<item: string>
          child 0, item: string
      child 1, indeterminate: string
      child 2, description: string
  child 2, organ: struct<index: list<item: string>, indeterminate: string, description: string>
      child 0, index: list<item: string>
          child 0, item: string
      child 1, indeterminate: string
      child 2, description: string
parents: struct<note: string, organ_to_region: struct<BRAIN: string, THYROID: string, LUNG_LEFT: string, LUNG (... 580 chars omitted)
  child 0, note: string
  chi
...
gion: string, organs: list<item: string>>
      child 0, region: string
      child 1, organs: list<item: string>
          child 0, item: string
  child 16, 41216001: struct<region: string, organs: list<item: string>>
      child 0, region: string
      child 1, organs: list<item: string>
          child 0, item: string
  child 17, 35039007: struct<region: string, organs: list<item: string>>
      child 0, region: string
      child 1, organs: list<item: string>
          child 0, item: string
  child 18, 15497006: struct<region: string, organs: list<item: string>>
      child 0, region: string
      child 1, organs: list<item: string>
          child 0, item: string
  child 19, 76752008: struct<region: string, organs: list<item: string>>
      child 0, region: string
      child 1, organs: list<item: string>
          child 0, item: string
  child 20, 71341001: struct<region: string, organs_unknown: list<item: string>>
      child 0, region: string
      child 1, organs_unknown: list<item: string>
          child 0, item: string
modality_tag: struct<note: string, US: string, IVUS: string, CT: string, MR: string, CR: string, DX: string, RF: s (... 78 chars omitted)
  child 0, note: string
  child 1, US: string
  child 2, IVUS: string
  child 3, CT: string
  child 4, MR: string
  child 5, CR: string
  child 6, DX: string
  child 7, RF: string
  child 8, XA: string
  child 9, MG: string
  child 10, PT: string
  child 11, NM: string
  child 12, OT: string
  child 13, SC: string
to
{'schema_version': Value('string'), 'taxonomy_version': Value('string'), 'unmapped_policy': Value('string'), 'unmapped_policy_note': Value('string'), 'modality_tag': {'note': Value('string'), 'US': Value('string'), 'IVUS': Value('string'), 'CT': Value('string'), 'MR': Value('string'), 'CR': Value('string'), 'DX': Value('string'), 'RF': Value('string'), 'XA': Value('string'), 'MG': Value('string'), 'PT': Value('string'), 'NM': Value('string'), 'OT': Value('string'), 'SC': Value('string')}, 'body_part_examined': {'note': Value('string'), 'ABDOMEN': {'region': Value('string')}, 'ABDOMENPELVIS': {'region': Value('string')}, 'ABDOMINALAORTA': {'region': Value('string'), 'organs': List(Value('string'))}, 'LIVER': {'region': Value('string'), 'organs': List(Value('string'))}, 'KIDNEY': {'region': Value('string'), 'organs_unknown': List(Value('string'))}, 'PANCREAS': {'region': Value('string'), 'organs': List(Value('string'))}, 'SPLEEN': {'region': Value('string'), 'organs': List(Value('string'))}, 'GALLBLADDER': {'region': Value('string'), 'organs': List(Value('string'))}, 'CHEST': {'region': Value('string')}, 'LUNG': {'region': Value('string'), 'organs_unknown': List(Value('string'))}, 'BREAST': {'region': Value('string'), 'organs': List(Value('string'))}, 'HEART': {'region': Value('string'), 'organs': List(Value('string'))}, 'THYROID': {'region': Value('string'), 'organs': List(Value('string'))}, 'NECK': {'region': Value('string')}, 'HEAD': {'region': Value('string')}, 'BRAIN': {'r
...
string'))}, 'Task05_Prostate': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task06_Lung': {'modality': Value('string'), 'region': Value('string'), 'organs_unknown': List(Value('string'))}, 'Task07_Pancreas': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task08_HepaticVessel': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task09_Spleen': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task10_Colon': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}}}, 'amos22': {'modality': Value('string'), 'modality_note': Value('string'), 'exhaustive_organ_labels': Value('bool'), 'min_mask_pixels': Value('int64'), 'structures': {'1': List(Value('string')), '2': List(Value('string')), '3': List(Value('string')), '4': List(Value('string')), '5': List(Value('string')), '6': List(Value('string')), '7': List(Value('string')), '8': List(Value('string')), '9': List(Value('string')), '10': List(Value('string')), '11': List(Value('string')), '12': List(Value('string')), '13': List(Value('string')), '14': List(Value('string')), '15': {'organs_unknown': List(Value('string')), 'note': Value('string')}}}}, 'region_from_slice_position': {'note': Value('string'), 'rules': List({'organ_present_any': List(Value('string')), 'region': Value('string')}), 'multi_region_rule': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema_version: string
              taxonomy_version: string
              evidence_build: null
              evidence_note: string
              promotion_thresholds: struct<note: string, model_trained_min_frames: int64, model_trained_min_patients: int64, model_train (... 86 chars omitted)
                child 0, note: string
                child 1, model_trained_min_frames: int64
                child 2, model_trained_min_patients: int64
                child 3, model_trained_min_sites: int64
                child 4, experimental_min_frames: int64
                child 5, experimental_min_patients: int64
              levels: struct<modality: struct<index: list<item: string>, indeterminate: string, description: string>, regi (... 168 chars omitted)
                child 0, modality: struct<index: list<item: string>, indeterminate: string, description: string>
                    child 0, index: list<item: string>
                        child 0, item: string
                    child 1, indeterminate: string
                    child 2, description: string
                child 1, region: struct<index: list<item: string>, indeterminate: string, description: string>
                    child 0, index: list<item: string>
                        child 0, item: string
                    child 1, indeterminate: string
                    child 2, description: string
                child 2, organ: struct<index: list<item: string>, indeterminate: string, description: string>
                    child 0, index: list<item: string>
                        child 0, item: string
                    child 1, indeterminate: string
                    child 2, description: string
              parents: struct<note: string, organ_to_region: struct<BRAIN: string, THYROID: string, LUNG_LEFT: string, LUNG (... 580 chars omitted)
                child 0, note: string
                chi
              ...
              gion: string, organs: list<item: string>>
                    child 0, region: string
                    child 1, organs: list<item: string>
                        child 0, item: string
                child 16, 41216001: struct<region: string, organs: list<item: string>>
                    child 0, region: string
                    child 1, organs: list<item: string>
                        child 0, item: string
                child 17, 35039007: struct<region: string, organs: list<item: string>>
                    child 0, region: string
                    child 1, organs: list<item: string>
                        child 0, item: string
                child 18, 15497006: struct<region: string, organs: list<item: string>>
                    child 0, region: string
                    child 1, organs: list<item: string>
                        child 0, item: string
                child 19, 76752008: struct<region: string, organs: list<item: string>>
                    child 0, region: string
                    child 1, organs: list<item: string>
                        child 0, item: string
                child 20, 71341001: struct<region: string, organs_unknown: list<item: string>>
                    child 0, region: string
                    child 1, organs_unknown: list<item: string>
                        child 0, item: string
              modality_tag: struct<note: string, US: string, IVUS: string, CT: string, MR: string, CR: string, DX: string, RF: s (... 78 chars omitted)
                child 0, note: string
                child 1, US: string
                child 2, IVUS: string
                child 3, CT: string
                child 4, MR: string
                child 5, CR: string
                child 6, DX: string
                child 7, RF: string
                child 8, XA: string
                child 9, MG: string
                child 10, PT: string
                child 11, NM: string
                child 12, OT: string
                child 13, SC: string
              to
              {'schema_version': Value('string'), 'taxonomy_version': Value('string'), 'unmapped_policy': Value('string'), 'unmapped_policy_note': Value('string'), 'modality_tag': {'note': Value('string'), 'US': Value('string'), 'IVUS': Value('string'), 'CT': Value('string'), 'MR': Value('string'), 'CR': Value('string'), 'DX': Value('string'), 'RF': Value('string'), 'XA': Value('string'), 'MG': Value('string'), 'PT': Value('string'), 'NM': Value('string'), 'OT': Value('string'), 'SC': Value('string')}, 'body_part_examined': {'note': Value('string'), 'ABDOMEN': {'region': Value('string')}, 'ABDOMENPELVIS': {'region': Value('string')}, 'ABDOMINALAORTA': {'region': Value('string'), 'organs': List(Value('string'))}, 'LIVER': {'region': Value('string'), 'organs': List(Value('string'))}, 'KIDNEY': {'region': Value('string'), 'organs_unknown': List(Value('string'))}, 'PANCREAS': {'region': Value('string'), 'organs': List(Value('string'))}, 'SPLEEN': {'region': Value('string'), 'organs': List(Value('string'))}, 'GALLBLADDER': {'region': Value('string'), 'organs': List(Value('string'))}, 'CHEST': {'region': Value('string')}, 'LUNG': {'region': Value('string'), 'organs_unknown': List(Value('string'))}, 'BREAST': {'region': Value('string'), 'organs': List(Value('string'))}, 'HEART': {'region': Value('string'), 'organs': List(Value('string'))}, 'THYROID': {'region': Value('string'), 'organs': List(Value('string'))}, 'NECK': {'region': Value('string')}, 'HEAD': {'region': Value('string')}, 'BRAIN': {'r
              ...
              string'))}, 'Task05_Prostate': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task06_Lung': {'modality': Value('string'), 'region': Value('string'), 'organs_unknown': List(Value('string'))}, 'Task07_Pancreas': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task08_HepaticVessel': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task09_Spleen': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}, 'Task10_Colon': {'modality': Value('string'), 'region': Value('string'), 'organs': List(Value('string'))}}}, 'amos22': {'modality': Value('string'), 'modality_note': Value('string'), 'exhaustive_organ_labels': Value('bool'), 'min_mask_pixels': Value('int64'), 'structures': {'1': List(Value('string')), '2': List(Value('string')), '3': List(Value('string')), '4': List(Value('string')), '5': List(Value('string')), '6': List(Value('string')), '7': List(Value('string')), '8': List(Value('string')), '9': List(Value('string')), '10': List(Value('string')), '11': List(Value('string')), '12': List(Value('string')), '13': List(Value('string')), '14': List(Value('string')), '15': {'organs_unknown': List(Value('string')), 'note': Value('string')}}}}, 'region_from_slice_position': {'note': Value('string'), 'rules': List({'organ_present_any': List(Value('string')), 'region': Value('string')}), 'multi_region_rule': Value('string')}}
              because column names don't match

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OrganScan training corpus — recipe and provenance

This repository publishes the recipe, not the pixels.

Medical imaging corpora carry licences that differ source by source, and several forbid redistribution outright. More importantly, frames rendered from DICOM can carry burned-in patient identifiers even after tag-level de-identification. Rather than ship images that are easy to misuse, this repo contains everything needed to rebuild the corpus from the pinned upstream releases.

What is here

File Purpose
sources.json Every source with its pinned revision, checksum, licence, access status, and training decision
anatomy-tiers.json The three-level taxonomy, the organ -> region parent map, and which classes are actually trained
anatomy-mappings.json Explicit decision for every upstream class and DICOM tag value; no default branch
canonical-frame-row.schema.json The row schema every source is normalised to
preprocessor.json Exact pixel handling, so a rebuild is byte-comparable
build-manifest.json Counts by split, source, modality, organ — and patients, not just frames

Corpus shape

Split Frames Patients
train 82,354 see build-manifest.json
validation 10,280 see build-manifest.json
test 15,395 see build-manifest.json
Modality Frames
CT 83,318
MR 1,880
US 14,907
XR 7,924

Why patient counts are printed next to frame counts

300 consecutive frames from one cine loop carry roughly the information of one sample. MedMNIST OrganAMNIST is 58,830 images from 201 CT scans — 293 images per patient. Training on the raw count inflates every metric and teaches the model scanner identity. sampling_policy in sources.json caps frames per patient before anything is written, and the builder enforces it.

Redistribution status by source

Source Licence Access Redistributed here
medmnist-plus-organamnist-224 CC-BY-4.0 open recipe only
medmnist-plus-organcmnist-224 CC-BY-4.0 open recipe only
medmnist-plus-organsmnist-224 CC-BY-4.0 open recipe only
medmnist-plus-breastmnist-224 CC-BY-4.0 open recipe only
medmnist-plus-chestmnist-224 CC-BY-4.0 open recipe only
msu-abdominal-ultrasound CC-BY-4.0 open_manual_browser_download recipe only
fetal-planes-db CC-BY-4.0 open recipe only
totalsegmentator-ct-sample CC-BY-4.0 open recipe only
totalsegmentator-ct-v300 CC-BY-4.0 open recipe only
amos22 CC-BY-4.0 open recipe only
medical-segmentation-decathlon CC-BY-SA-4.0 open_but_unresolved_host recipe only
rocov2 CC-BY-NC-4.0 open no — licence forbids
radimagenet undefined registration_and_agreement_required no — licence forbids
camus undefined registration_required no — licence forbids
echonet-dynamic Stanford EchoNet-Dynamic Dataset Research Use Agreement signed_research_use_agreement no — licence forbids
echonet-lvh Stanford EchoNet-LVH Dataset Research Use Agreement signed_research_use_agreement no — licence forbids
busi-original undefined mirror_only no — licence forbids
tn3k undefined third_party_drive no — licence forbids
ddti undefined source_site_unavailable no — licence forbids
mmotu undefined third_party_drive no — licence forbids
pocus-covid19-ultrasound mixed per-file (CC-BY-4.0, CC-BY-NC-4.0, all-rights-reserved) open no — licence forbids
biomedclip MIT tag, but the model card restricts use open no — licence forbids
usfm CC-BY-NC-4.0 third_party_drive no — licence forbids

Rebuild it 

git clone https://github.com/palarnab/organscan && cd organscan
python downloaded-training-data/download.py     # pinned revisions, checksum-verified
python scripts/build_frame_dataset.py
python scripts/validate_frame_dataset.py        # leakage, mask coherence, coverage

The build fails rather than silently dropping data when an unmapped upstream class appears, and validation fails on patient leakage across splits, identical frames in two splits, or a weak label found in an evaluation split.

Attribution

Every source below requires credit, and every one of them was modified — sliced from volumes or cine loops, windowed, resized to 224×224, capped per patient, and relabelled into the OrganScan taxonomy. Full per-source detail, including the exact modifications, is in sources.json under each entry's attribution block.

Source Creators Licence Frames used
TotalSegmentator CT dataset v3.0.0 Jakob Wasserthal et al. CC-BY-4.0 61,902
AMOS22: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation Yuanfeng Ji et al. CC-BY-4.0 11,480
FETAL_PLANES_DB: Common maternal-fetal ultrasound images Xavier P. Burgos-Artizzu et al. CC-BY-4.0 10,748
MedMNIST v2 (ChestMNIST, 224px) Jiancheng Yang et al. CC-BY-4.0 7,924
MedMNIST v2 (OrganSMNIST, 224px) Jiancheng Yang et al. CC-BY-4.0 4,000
MedMNIST v2 (OrganAMNIST, 224px) Jiancheng Yang et al. CC-BY-4.0 3,914
MedMNIST v2 (OrganCMNIST, 224px) Jiancheng Yang et al. CC-BY-4.0 3,902
Abdominal Ultrasound Image Dataset for Organ Classification and Disease Detection Sifat Zina Karim CC-BY-4.0 3,380
MedMNIST v2 (BreastMNIST, 224px) Jiancheng Yang et al. CC-BY-4.0 779 
OrganScan is trained on the following datasets, all of which were modified
(sliced to 2D, windowed, resized to 224px, and relabelled):

- TotalSegmentator CT dataset v3.0.0
  Jakob Wasserthal, Hanns-Christian Breit, Manfred T. Meyer, Maurice Pradella, Daniel Hinck, Alexander W. Sauter, Tobias Heye, Daniel T. Boll, Joshy Cyriac, Shan Yang, Michael Bach, Martin Segeroth - CC-BY-4.0
  https://zenodo.org/records/22688904
- AMOS22: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
  Yuanfeng Ji, Haotian Bai, Chongjian Ge, Jie Yang, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, Ping Luo - CC-BY-4.0
  https://zenodo.org/records/7262581
- FETAL_PLANES_DB: Common maternal-fetal ultrasound images
  Xavier P. Burgos-Artizzu, David Coronado-Gutierrez, Brenda Valenzuela-Alcaraz, Elisenda Bonet-Carne, Elisenda Eixarch, Fatima Crispi, Eduard Gratacós - CC-BY-4.0
  https://zenodo.org/records/3904280
- MedMNIST v2 (ChestMNIST, 224px)
  Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni - CC-BY-4.0
  https://zenodo.org/records/10519652
- MedMNIST v2 (OrganSMNIST, 224px)
  Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni - CC-BY-4.0
  https://zenodo.org/records/10519652
- MedMNIST v2 (OrganAMNIST, 224px)
  Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni - CC-BY-4.0
  https://zenodo.org/records/10519652
- MedMNIST v2 (OrganCMNIST, 224px)
  Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni - CC-BY-4.0
  https://zenodo.org/records/10519652
- Abdominal Ultrasound Image Dataset for Organ Classification and Disease Detection
  Sifat Zina Karim - CC-BY-4.0
  https://scholarsjunction.msstate.edu/research-data/5/
- MedMNIST v2 (BreastMNIST, 224px)
  Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni - CC-BY-4.0
  https://zenodo.org/records/10519652

Author

Arnab Pal — LinkedIn · GitHub
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