---
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- retinal
- fundus
- vessel-segmentation
- ophthalmology
size_categories:
- n<1K
---

# CHASE_DB1

Retinal blood vessel segmentation from color fundus photographs, collected in the
**Child Heart and Health Study in England (CHASE)**.

## Contents

- **28 images** = 14 children x 2 eyes (left/right)
- **999 x 960** RGB fundus photographs (Nidek NM-200-D handheld camera, 30 deg FOV)
- **Two independent manual vessel annotations per image**

| Column | Description |
|---|---|
| `image` | RGB fundus photograph |
| `mask` | **1st human observer (1stHO) - the gold-standard ground truth.** Binary, `0` = background, `1` = vessel |
| `mask_2ndHO` | 2nd human observer. Binary, same encoding. **A human-performance baseline, not the GT** |
| `image_id` | e.g. `Image_01L` |
| `subject_id` | e.g. `01` - the grouping key (see split caveat below) |
| `eye` | `L` or `R` |

### Which mask is the ground truth?

Use **`mask` (1stHO)**. This is the tier that Fraz et al. (2012) and effectively all
subsequent CHASE_DB1 literature train and evaluate against. The second observer's
annotations exist so papers can report a "human observer" row.

Measured on this archive: mean vessel fraction is **6.93% (1stHO)** vs **6.64% (2ndHO)**,
and **inter-observer Dice is 0.777** (range 0.739-0.827). That ~0.78 is the effective
human ceiling on this dataset - a model scoring below it is not necessarily failing.

## Splits

The official archive is **completely flat and ships no split.** This upload applies the
**community-standard 20 train / 8 test** convention (the "first 20 images" split), which
in sorted order works out to subjects 01-10 for training and subjects 11-14 for testing.

**Split caveat:** each child contributes two eyes, so a random 28-way split leaks fellow
eyes across train and test. Always group by `subject_id`. The split above is
**subject-disjoint** by construction.

Two other conventions appear in the wild and are *not* used here - be careful when
comparing numbers across papers:
- Idiap `bob`/`mednet` uses **8 train / 20 test** (reversed)
- At least one HuggingFace mirror uses a 14/14 split

## Field-of-view masks

**None.** CHASE_DB1 ships no official FOV/circular masks. FOV masks distributed with some
third-party packages are generated by those packages, not by the dataset authors, so no
`fov_mask` column is included here rather than passing synthetic annotation off as
dataset content.

## Provenance

The original Kingston University download pages are no longer reachable
(`blogs.kingston.ac.uk/retinal/chasedb1/` no longer resolves; `researchdata.kingston.ac.uk`
has a broken DNS delegation; the `staffnet` mirror now returns 403). This upload was
built from the archived copy of the official EPrints record, and its integrity was
confirmed by checking it byte-for-byte against the independently archived copy from the
second official Kingston host - **both are identical** (`md5 d9e47c4bac125b29996fae4380a68db1`,
`sha256 bb750b73633492d700e56d91578ee3cdb7e4dbd08279e7465583e9efd26790b6`). The 28-image
count matches the count reported in the paper.

The only transformation applied is re-encoding the 1-bit mask PNGs as 8-bit PNGs with
values `{0, 1}`; image pixels and mask geometry are unchanged.

### Note on a common misstatement

Several secondary sources describe CHASE_DB1 as *"56 images from 28 subjects (14 healthy,
14 diabetic retinopathy)."* This is **incorrect.** It is 28 images from 14 healthy
schoolchildren; there is no diabetic-retinopathy cohort.

### Resolution

The authors quote 1280x960 as the camera's capture resolution, and many papers repeat it,
but every distributed file - all 28 images and all 56 masks - is **999x960**.

## Overlap with other retinal datasets

None known. CHASE_DB1 is a UK schoolchildren cardiovascular cohort, disjoint in population,
country, camera, and era from DRIVE (Netherlands), STARE (US), HRF (Germany), IDRiD (India),
PAPILA (Spain), and RAVIR (infrared SLO). RITE is derived from DRIVE, not CHASE.

## License

**CC BY 4.0.** Copyright Kingston University and St George's, University of London.
Redistribution is permitted with attribution.

## Citation

```bibtex
@article{fraz2012ensemble,
  title   = {An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation},
  author  = {Fraz, Muhammad Moazam and Remagnino, Paolo and Hoppe, Andreas and
             Uyyanonvara, Bunyarit and Rudnicka, Alicja R. and Owen, Christopher G. and
             Barman, Sarah A.},
  journal = {IEEE Transactions on Biomedical Engineering},
  volume  = {59},
  number  = {9},
  pages   = {2538--2548},
  year    = {2012},
  doi     = {10.1109/TBME.2012.2205687}
}
```