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CHASE DB1 Dataset

2812650
Tagmedical
Tasksemantic segmentation
Release YearMade in 2012
LicenseCC BY 4.0
Download2 MB

Introduction #

Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppeet al.

The Child Heart and Health Study in England (CHASE), is a cardiovascular health survey across 200 primary schools in London, Birmingham, and Leicester. The retinal imaging was carried out in 46 schools and demonstrated associations between retinal vessel tortuosity and early risk factors for cardiovascular disease in over 1000 British primary school children of different ethnic origin. The retinal images of both of the eyes of each child were recorded with a hand-held Nidek NM-200-D fundus camera. The images were captured at 30 degree field-of-view with a resolution of 1280 Γ— 960 pixels. The dataset of images are characterized by having nonuniform background illumination, poor contrast of blood vessels as compared with the background and wider arteriolars that have a bright strip running down the centre known as the central vessel reflex. This work is based on using a subset of images to create a retinal vessel reference dataset representing multi-ethnic school children known as CHASE_DB1. 28 retinal images are contained, acquired from both eyes of 14 children (8 white, 3 South Asian, 3 of other ethnic origin, mean age 10 years) recruited from one primary school in North-East London. For each image, there are two ground truths images for vessel segmentation made by two independent human observers.

File naming convention:

"01"-"14" = participant number.
"L" = left eye.
"R" = right eye.
"1stHO" = ground truth from first human observer.
"2ndHO" = ground truth from second human observer.
ExpandExpand
Dataset LinkHomepageDataset LinkResearch Paper

Summary #

CHASE DB1: Retinal Vessel Reference Dataset is a dataset for a semantic segmentation task. It is used in the medical research.

The dataset consists of 28 images with 28 labeled objects belonging to 1 single class (vessel).

Images in the CHASE DB1 dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2012 by the Kingston University, London and St. George’s, University of London.

Here is the visualized example grid with animated annotations:

Explore #

CHASE DB1 dataset has 28 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
OpenSample annotation mask from CHASE DB1Sample image from CHASE DB1
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Have a look at 28 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
vesselβž”
mask
28
28
1
6.93%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-1 of 1
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
vessel
mask
28
6.93%
8.52%
5.06%
873px
90.94%
926px
96.46%
911px
94.87%
798px
79.88%
922px
92.29%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 28 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 28
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
vessel
mask
Image_14R.jpg
960 x 999
905px
94.27%
798px
79.88%
5.85%
2βž”
vessel
mask
Image_14L.jpg
960 x 999
909px
94.69%
860px
86.09%
6.88%
3βž”
vessel
mask
Image_09R.jpg
960 x 999
911px
94.9%
882px
88.29%
5.09%
4βž”
vessel
mask
Image_02R.jpg
960 x 999
920px
95.83%
913px
91.39%
7.81%
5βž”
vessel
mask
Image_04R.jpg
960 x 999
926px
96.46%
887px
88.79%
7.7%
6βž”
vessel
mask
Image_03R.jpg
960 x 999
919px
95.73%
904px
90.49%
7.55%
7βž”
vessel
mask
Image_07R.jpg
960 x 999
924px
96.25%
915px
91.59%
7.61%
8βž”
vessel
mask
Image_08L.jpg
960 x 999
873px
90.94%
879px
87.99%
6.47%
9βž”
vessel
mask
Image_09L.jpg
960 x 999
900px
93.75%
821px
82.18%
5.06%
10βž”
vessel
mask
Image_10L.jpg
960 x 999
898px
93.54%
877px
87.79%
6.26%

License #

CHASE DB1: Retinal Vessel Reference Dataset is under CC BY 4.0 license.

Source

Citation #

If you make use of the CHASE DB1 data, please cite the following reference:

@dataset{CHASE DB1,
	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.},
	title={CHASE DB1: Retinal Vessel Reference Dataset},
	year={2012},
	url={https://researchdata.kingston.ac.uk/96/}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-chase-db1-dataset,
  title = { Visualization Tools for CHASE DB1 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/chase-db1 } },
  url = { https://datasetninja.com/chase-db1 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { may },
  note = { visited on 2024-05-26 },
}

Download #

Dataset CHASE DB1 can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='CHASE DB1', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

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