Datasets:

EvidenceAIResearch
/

radgenome-anatomy

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
volume_id: string
split: string
image_pa: struct<bytes: binary, path: string>
  child 0, bytes: binary
  child 1, path: string
  -- field metadata --
  huggingface: '{"_type":"Image"}'
image_ll: struct<bytes: binary, path: string>
  child 0, bytes: binary
  child 1, path: string
  -- field metadata --
  huggingface: '{"_type":"Image"}'
mask_labels_pa: list<element: string>
  child 0, element: string
masks_pa: list<element: struct<bytes: binary, path: string>>
  child 0, element: struct<bytes: binary, path: string>
      child 0, bytes: binary
      child 1, path: string
mask_labels_ll: list<element: string>
  child 0, element: string
masks_ll: list<element: struct<bytes: binary, path: string>>
  child 0, element: struct<bytes: binary, path: string>
      child 0, bytes: binary
      child 1, path: string
-- schema metadata --
huggingface: '{"info": {"features": {"image_pa": {"_type": "Image"}, "ima' + 161
to
{'volume_id': Value('string'), 'split': Value('string'), 'image_pa': Image(mode=None, decode=True), 'image_ll': Image(mode=None, decode=True)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              volume_id: string
              split: string
              image_pa: struct<bytes: binary, path: string>
                child 0, bytes: binary
                child 1, path: string
                -- field metadata --
                huggingface: '{"_type":"Image"}'
              image_ll: struct<bytes: binary, path: string>
                child 0, bytes: binary
                child 1, path: string
                -- field metadata --
                huggingface: '{"_type":"Image"}'
              mask_labels_pa: list<element: string>
                child 0, element: string
              masks_pa: list<element: struct<bytes: binary, path: string>>
                child 0, element: struct<bytes: binary, path: string>
                    child 0, bytes: binary
                    child 1, path: string
              mask_labels_ll: list<element: string>
                child 0, element: string
              masks_ll: list<element: struct<bytes: binary, path: string>>
                child 0, element: struct<bytes: binary, path: string>
                    child 0, bytes: binary
                    child 1, path: string
              -- schema metadata --
              huggingface: '{"info": {"features": {"image_pa": {"_type": "Image"}, "ima' + 161
              to
              {'volume_id': Value('string'), 'split': Value('string'), 'image_pa': Image(mode=None, decode=True), 'image_ll': Image(mode=None, decode=True)}
              because column names don't match

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RadGenome-Anatomy

RadGenome-Anatomy is a large-scale chest radiograph anatomy segmentation dataset constructed from the RadGenome-ChestCT corpus (originally based on CT-RATE). It contains 25,692 volumetric studies (24,128 train / 1,564 validation), yielding paired postero-anterior (PA) and lateral (LL) projection images at 384 × 384 resolution.

Across the two radiographic views, the dataset provides 10,790,646 fine-grained anatomy masks over 210 canonical anatomy classes and 513,860 region masks over 10 anatomical groups, for a total of 11,304,506 binary mask instances.

Each row represents one CT study and contains its PA and LL projection images.

Dataset Summary

Property	Value
Studies	25,692 total (24,128 train / 1,564 val)
Views per study	2 (PA + LL)
Image resolution	384 × 384
Anatomy classes	210 structures (4-level hierarchy)
Region classes	10 body-system groups
Anatomy masks	10,790,646 (5,395,323 PA + 5,395,323 LL)
Region masks	513,860 (256,930 PA + 256,930 LL)
License	CC-BY-4.0
Source	RadGenome-ChestCT / CT-RATE

Splits

Split	Studies	PA projections	LL projections	Anatomy masks	Region masks
train	24,128	24,129	24,129	10,133,770	482,580
validation	1,564	1,564	1,564	656,876	31,280
total	25,692	25,693	25,693	10,790,646	513,860

Dataset Structure

Data Fields

Column	Type	Description
`volume_id`	`str`	Unique study identifier, e.g. `train_1_a_1`.
`split`	`str`	Dataset split: `train` or `validation`.
`image_pa`	`Image`	PA (posteroanterior, front) chest projection image (JPEG, 384×384).
`image_ll`	`Image`	LL (lateral, side) chest projection image (JPEG, 384×384).

Anatomy Label Universe

The dataset defines 210 canonical anatomy classes organized as a four-level hierarchy: body system → organ → substructure → canonical label. At the top level, classes are grouped into 10 body systems, with a highly non-uniform per-system class count:

Body system	# classes	Example structures
Skeletal	93	ribs (1–12 L/R), thoracic vertebrae (T1–T12), cervical/lumbar vertebrae, sternum, clavicles, scapulae, humerus, femur
Abdominal	42	liver (with segments), spleen, pancreas, kidneys, gallbladder, stomach, intestine
Mediastinal	25	aorta, IVC/SVC, carotid/subclavian arteries, brachiocephalic vessels, iliac/renal vessels
Cardiac	11	heart, atria (L/R), ventricles (L/R), myocardium, ascending aorta, left auricle, heart tissue
Pulmonary	15	left/right lung, upper/middle/lower lobes (L/R), lung nodule, tumor, effusion, pulmonary vein, pulmonary embolism
Airway	6	trachea, bronchi, larynx (glottis, supraglottis), cricopharyngeal inlet
Endocrine	8	thyroid (L/R + gland), adrenal glands (L/R), thymus
Esophageal	2	esophagus structures
Breast	3	breast structures
Neural / soft tissue	5	spinal cord, skin, muscle

These same 10 body systems also serve as the region-mask label set (10 classes/view).

The full ordered list of canonical labels is in label_universe.json at the repo root. Use it to map labels to fixed class indices for consistent multi-label training.

Usage

Load with 🤗 Datasets

from datasets import load_dataset

ds = load_dataset("EvidenceAIResearch/radgenome-anatomy")
print(ds)

Access images

from PIL import Image
import io

row = ds["train"][0]
pa_img = Image.open(io.BytesIO(row["image_pa"]["bytes"]))
ll_img = Image.open(io.BytesIO(row["image_ll"]["bytes"]))

License

CC-BY-4.0 — derived from CT-RATE. Commercial use is not permitted without prior permission from the original data providers. See the original dataset terms for full conditions.

Citation

@article{ye2026radgenome,
  title={RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection},
  author={Ye, Shuchang and Meng, Mingyuan and Wang, Hao and Naseem, Usman and Kim, Jinman},
  journal={arXiv preprint arXiv:2605.17368},
  year={2026}
}

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Paper for EvidenceAIResearch/radgenome-anatomy

RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection

Paper • 2605.17368 • Published May 17