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.

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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

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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.

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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.