Datasets:

MedOtter
/

DRAC22

license: cc-by-4.0
pretty_name: DRAC22
task_categories:
  - image-segmentation
  - image-classification
tags:
  - medical-imaging
  - retina
  - octa
  - uw-octa
  - diabetic-retinopathy
  - ophthalmology
  - miccai-2022
size_categories:
  - 1K<n<10K
configs:
  - config_name: segmentation
    data_files:
      - split: train
        path: segmentation/train-*
      - split: test_unlabeled
        path: segmentation/test_unlabeled-*
  - config_name: image_quality_assessment
    data_files:
      - split: train
        path: image_quality_assessment/train-*
      - split: test_unlabeled
        path: image_quality_assessment/test_unlabeled-*
  - config_name: dr_grading
    data_files:
      - split: train
        path: dr_grading/train-*
      - split: test_unlabeled
        path: dr_grading/test_unlabeled-*

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.