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High Resolution Fundus Dataset

4521
Tagmedical
Tasksemantic segmentation
Release YearMade in 2013
LicenseCC BY 4.0
Download67 MB

Introduction #

Attila Budai, Jan Odstrcilik, R. Kollaret al.

The authors of the High Resolution Fundus (HRF) Image Database have a goal to create a database of fundus images with gold standards for the task of segmentation. The dataset contains 15 images of healthy patients, 15 images of patients with diabetic retinopathy, and 15 images of glaucomatous patients. Binary gold standard vessel segmentation images are available for each. Also, the masks determining field of view (FOV) are provided for particular datasets. The gold standard data is generated by a group of experts working in the field of retinal image analysis and clinicians from the cooperated ophthalmology clinics.

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Dataset LinkHomepageDataset LinkPoster

Summary #

High Resolution Fundus (HRF) Image Database is a dataset for a semantic segmentation task. It is used in the medical research.

The dataset consists of 45 images with 90 labeled objects belonging to 2 different classes including vessels and field of view.

Images in the High Resolution Fundus 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. Alternatively, the dataset could be split into 3 categories: diabetic retinopathy (15 images), glaucomatous (15 images), and healthy (15 images). The dataset was released in 2013 by the GE-CZ joint research group.

Dataset Poster

Explore #

High Resolution Fundus dataset has 45 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 High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
OpenSample annotation mask from High Resolution FundusSample image from High Resolution Fundus
πŸ‘€
Have a look at 45 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
vesselsβž”
mask
45
45
1
7.71%
field of viewβž”
mask
45
45
1
84.46%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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 1-2 of 2
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
vessels
mask
45
7.71%
10.56%
5.09%
2336px
100%
2336px
100%
2336px
100%
3156px
90.07%
3263px
93.12%
field of view
mask
45
84.46%
84.5%
84.42%
2336px
100%
2336px
100%
2336px
100%
3261px
93.07%
3263px
93.12%

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 90 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 90
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”
field of view
mask
07_g.jpg
2336 x 3504
2336px
100%
3262px
93.09%
84.46%
2βž”
vessels
mask
07_g.jpg
2336 x 3504
2336px
100%
3256px
92.92%
6.51%
3βž”
field of view
mask
07_h.jpg
2336 x 3504
2336px
100%
3262px
93.09%
84.46%
4βž”
vessels
mask
07_h.jpg
2336 x 3504
2336px
100%
3218px
91.84%
8.91%
5βž”
field of view
mask
09_g.jpg
2336 x 3504
2336px
100%
3263px
93.12%
84.49%
6βž”
vessels
mask
09_g.jpg
2336 x 3504
2336px
100%
3261px
93.07%
6.62%
7βž”
field of view
mask
12_h.jpg
2336 x 3504
2336px
100%
3262px
93.09%
84.45%
8βž”
vessels
mask
12_h.jpg
2336 x 3504
2336px
100%
3254px
92.87%
10.48%
9βž”
field of view
mask
12_dr.JPG
2336 x 3504
2336px
100%
3263px
93.12%
84.47%
10βž”
vessels
mask
12_dr.JPG
2336 x 3504
2336px
100%
3250px
92.75%
6.8%

License #

High Resolution Fundus (HRF) Image Database is under CC BY 4.0 license.

Source

Citation #

If you make use of the High Resolution Fundus data, please cite the following reference:

@dataset{High Resolution Fundus,
  author={Attila Budai and Jan Odstrcilik},
  title={High Resolution Fundus (HRF) Image Database},
  year={2013},
  url={https://www5.cs.fau.de/research/data/fundus-images/}
}

Source

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

@misc{ visualization-tools-for-high-resolution-fundus-dataset,
  title = { Visualization Tools for High Resolution Fundus Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/high-resolution-fundus } },
  url = { https://datasetninja.com/high-resolution-fundus },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset High Resolution Fundus 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='High Resolution Fundus', 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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