DRAC22 — Diabetic Retinopathy Analysis Challenge 2022

Ultra-wide optical coherence tomography angiography (UW-OCTA) benchmark from MICCAI 2022. 1,103 images, 12x12 mm en-face projections of the inner retinal layer centered on the fovea, acquired on a VG200D swept-source OCTA device at Shanghai Sixth People's Hospital. All images are 1024x1024 8-bit grayscale PNG.

This is a faithful re-hosting of the complete official Zenodo release (10.5281/zenodo.10280358), reorganized into three parquet configs. No images or labels were dropped, resampled, or re-encoded.

Configs

Config	Task	Train	Test (unlabeled)	Ground truth
segmentation	Task 1 — DR lesion segmentation	109	65	pixel masks
image_quality_assessment	Task 2 — image quality	665	438	3-class label
dr_grading	Task 3 — DR grading	611	386	3-class label

image_quality_assessment covers the full 1,103-image pool; the other two tasks are subsets of it. Only segmentation carries pixel-level ground truth.

segmentation — read this before evaluating

The three lesion classes are three independent binary masks, not one multi-class label map, and they are per-class subsets of the 109 training images:

Class	Column	Train masks	Median lesion area
1 — Intraretinal Microvascular Abnormalities (IRMA)	mask_irma	86	0.47 %
2 — Nonperfusion Areas (NPA)	mask_npa	106	15.0 %
3 — Neovascularization (NV)	mask_nv	35	0.60 %
		227 total

Co-occurrence: IRMA&NPA 83 · IRMA&NV 33 · NPA&NV 33 · all three 31. Every training image has at least one mask, but only 31 of 109 are annotated for all three classes.

An absent mask means the class is genuinely absent from that image — it is not an all-background label. Absent classes are stored as NULL, never as zero masks. Use the has_irma / has_npa / has_nv booleans to select which images are scorable for a given class. Treating a NULL as empty ground truth injects 100%-empty references into per-class DSC and silently corrupts the metric.

The classes also overlap spatially — IRMA and NPA intersect in 73 of the 83 images carrying both (215,065 px); NPA and NV in 22 of 33 — so flattening them into a single integer label map is lossy.

Verified over all 227 masks: each is 1024x1024, strictly binary {0, 255}, non-empty, and its id exists in the training image set.

Columns (segmentation)

Column	Type	Description
image	Image	UW-OCTA en-face image, 1024x1024 grayscale
mask_irma	Image | null	Binary IRMA mask (0/255), NULL if class absent
mask_npa	Image | null	Binary nonperfusion-area mask (0/255), NULL if class absent
mask_nv	Image | null	Binary neovascularization mask (0/255), NULL if class absent
has_irma, has_npa, has_nv	bool	Whether that class is annotated for this image
num_classes_annotated	int	1–3 (train); 0 for test_unlabeled
image_id	string	Global id, e.g. 082
image_quality_level	int | null	Cross-ref from Task 2: 0 Poor / 1 Good / 2 Excellent
dr_grade	int | null	Cross-ref from Task 3: 0 Non-DR / 1 NPDR / 2 PDR
split	string	train or test_unlabeled

Classification configs share image, image_id, label (that task's own label), image_quality_level, dr_grade, has_segmentation, and split.

Splits and withheld test labels

Test ground truth for all three tasks is withheld on the Grand-Challenge leaderboard, which remains open for post-challenge submission. The test images are included here for inference and submission, but under the split name test_unlabeled so they cannot be mistaken for a scorable split. Every label/mask column is NULL there.

The usable segmentation set is the 109 training images.

Ground truth

Two ophthalmologists annotated every image independently; disagreements were arbitrated by two additional, more senior ophthalmologists. Only this consensus is released — no per-rater masks exist, so no inter-rater analysis is possible. The challenge metric is per-class DSC, with IoU as tie-break.

Cross-task linkage

Filenames form a globally unique id space across the release (verified: zero id collisions between the train and test pools). Task 1's 174 images are byte-identical to their Task 2 counterparts (verified by md5 over all 174), and the train/test partition is consistent across tasks. Each config therefore carries the other tasks' labels as cross-reference columns — e.g. filter segmentation samples by dr_grade.

Caveats

• No patient metadata exists. The release ships no age, gender, eye, or history fields; the authors state they cannot report the number of eyes or patients. Images cannot be grouped by patient, so patient-level splitting is impossible and any train/val split of the 109 images may leak at the eye or patient level.
• IRMA and NV are very small targets (median 0.47 % and 0.60 % of the image). Expect low scores from promptless or box-prompted general-purpose segmenters.
• No overlap with OCTA-500 (Nanjing, 3/6 mm, 304 px) or ROSE (Ningbo CAS), and none with the color-fundus datasets DRIVE / IDRiD / PAPILA / RAVIR. DeepDRiD shares senior authors and the same hospital clinic but is UWF color fundus with no masks, so there is no segmentation-benchmark contamination.

Source

• Paper: Qian B, Chen H, Wang X, et al. "DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images." Patterns 5(3), 100929 (2024). doi:10.1016/j.patter.2024.100929
• Data: Zenodo 10.5281/zenodo.10280358
• Challenge: https://drac22.grand-challenge.org/

License

CC BY 4.0, as published on the Zenodo deposit. Redistribution with attribution is permitted; please cite the Patterns paper above.