Datasets:
license: cc-by-4.0
task_categories:
- image-segmentation
- image-classification
language:
- en
tags:
- medical
- radiology
- chest-ct
- anatomy
- segmentation
- ct-scan
- projection
pretty_name: RadGenome-Anatomy
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/train/*.parquet
- split: validation
path: data/validation/*.parquet
dataset_info:
features:
- name: volume_id
dtype: string
- name: split
dtype: string
- name: image_pa
dtype: image
- name: image_ll
dtype: image
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}
}