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STARE Dataset

3971184
Tagmedical
Tasksemantic segmentation
Release YearMade in 2000
Licensepublicly available
Download216 MB

Introduction #

A. Hoover, V. Kouznetsova, M. Goldbaum

The STARE: Structured Analysis of the Retina Dataset concerns a system to automatically diagnose diseases of the human eye. It was conceived and initiated in 1975. During its history, over thirty people contributed to the project, with backgrounds ranging from medicine to science to engineering. Images and clinical data were provided by the Shiley Eye Center at the University of California, San Diego, and by the Veterans Administration Medical Center in San Diego.

Motivation

Blood vessel appearance is an important indicator for many diagnoses, including diabetes, hypertension, and arteriosclerosis. Vessels and arteries have many observable features, including diameter, color, tortuosity (relative curvature), and opacity (reflectivity). Artery-vein crossings and patterns of small vessels can also serve as diagnostic indicators. An accurate delineation of the boundaries of blood vessels makes precise measurements of these features possible. These measurements may then be applied to a variety of tasks, including diagnosis, treatment evaluation, and clinical study.

Dataset description

The authors present an automated approach for identifying and delineating blood vessels within ocular fundus images. This tool offers the potential to facilitate broader screenings for vessel abnormalities among populations by eyecare specialists. Additionally, it enables more precise measurements, which are crucial for treatment evaluation and clinical research. Moreover, utilizing such a tool ensures a more systematic and reproducible approach to observations. Unlike previous methods that focused mainly on local attributes, this method considers various characteristics of vessels such as color, shape, gradient, and contrast with the background.

image

In a healthy retina, the optic nerve has a readily identifiable size, shape, color, and location relative to the blood vessels.

The optic nerve appears toward the of this image as a circular area, roughly one-sixth the width of the image in diameter, brighter than the surrounding area, as the convergent area of the blood vessel network. In an image of a healthy retina, all these properties (shape, color, size, convergence) help contribute to the identification of the nerve. However, these features show a large variance that makes simple detection methods brittle, particularly in the presence of retinal disease.

image

Retina containing lesions of the same brightness as the nerve.

This image of the retina containing drusen. The brightness of these lesions overlaps the brightness of the nerve, so that using brightness as a lone feature for detection is difficult.

image

Swollen nerve, showing a distorted size and shape.

image

Bright circular lesion that looks similar to an optic nerve.

image

Nerve that is completely obscured by hemorrhaging.

These cases underscore the challenge in detecting the optic nerve and emphasize the necessity for a robust method capable of functioning effectively in the presence of various retinal diseases. Since the optic nerve’s most consistent feature is its role as the convergence point of the blood vessel network, the authors have developed a method for optic nerve detection based on identifying this convergence. When a unique and strongly identifiable convergence isn’t present, brightness serves as a secondary feature for optic nerve detection.

The authors employ multiple vessel segmentations of the same image to enhance the detection of convergent points. The concept is that the convergence should be discernible across vessel segmentations at different scales. To locate the convergence of the vessel network, they introduce a novel algorithm named “fuzzy convergence.” This algorithm operates as a voting-type method within the spatial domain of the image. The input consists of a binary segmentation of the blood vessels, with each vessel being represented by a fuzzy segment contributing to a cumulative voting image. The algorithm outputs a convergence image, which is then thresholded to identify the most prominent point(s) of convergence.

image

Binary segmentation of blood vessels. (a) Sparser scale. (b) Denser scale.

The authors rely primarily on a pioneering algorithm dubbed “fuzzy convergence” for optic nerve detection. This innovative approach pinpoints the optic nerve as the central hub of the blood vessel network. When a distinct convergence point is lacking, the authors’ method identifies the optic nerve as the brightest area in the image following illumination equalization.

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Dataset LinkHomepageDataset LinkResearch Paper 1Dataset LinkResearch Paper 2Dataset LinkAcademic Torrents

Summary #

STARE: Structured Analysis of the Retina Dataset is a dataset for a semantic segmentation task. It is used in the medical research.

The dataset consists of 397 images with 60 labeled objects belonging to 1 single class (vessels).

Images in the STARE dataset have pixel-level semantic segmentation annotations. There are 377 (95% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 3 labelers: adam hoover (20 objects), filter probing algorithm (20 objects), and valentina kouznetsova (20 objects). Additionally, every image contains information about patient diagnos. Explore it in supervisely labeling tool. The dataset was released in 2000 by the University of California, USA.

Dataset Poster

Explore #

STARE dataset has 397 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 STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
OpenSample annotation mask from STARESample image from STARE
👀
Have a look at 397 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
Images
Objects
Count on image
average
Area on image
average
vessels
mask
20
60
3
13.54%

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
vessels
mask
60
8.81%
15.28%
4.31%
505px
83.47%
559px
92.4%
545px
90.12%
604px
86.29%
684px
97.71%

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 60 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 60
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
vessels
mask
im0077.png
605 x 700
551px
91.07%
646px
92.29%
8.01%
2
vessels
mask
im0077.png
605 x 700
552px
91.24%
647px
92.43%
12.99%
3
vessels
mask
im0077.png
605 x 700
535px
88.43%
636px
90.86%
8.07%
4
vessels
mask
im0044.png
605 x 700
557px
92.07%
665px
95%
6.96%
5
vessels
mask
im0044.png
605 x 700
559px
92.4%
684px
97.71%
11.42%
6
vessels
mask
im0044.png
605 x 700
539px
89.09%
670px
95.71%
8.18%
7
vessels
mask
im0004.png
605 x 700
545px
90.08%
615px
87.86%
7.41%
8
vessels
mask
im0004.png
605 x 700
544px
89.92%
633px
90.43%
7.23%
9
vessels
mask
im0004.png
605 x 700
528px
87.27%
619px
88.43%
8.67%
10
vessels
mask
im0005.png
605 x 700
555px
91.74%
633px
90.43%
9.03%

License #

The STARE: Structured Analysis of the Retina Dataset is publicly available.

Source

Citation #

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

@dataset{STARE,
  author={A. Hoover and V. Kouznetsova and M. Goldbaum},
  title={STARE: Structured Analysis of the Retina Dataset},
  year={2000},
  url={https://cecas.clemson.edu/~ahoover/stare/}
}

Source

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

@misc{ visualization-tools-for-stare-dataset,
  title = { Visualization Tools for STARE Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/stare } },
  url = { https://datasetninja.com/stare },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset STARE 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='STARE', 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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