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EDD2020 Dataset

386101
Tagmedical
Taskinstance segmentation
Release YearMade in 2020
LicenseCC BY-NC 4.0
Download59 MB

Introduction #

Released 2020-06-03 ·Sharib Ali, Barbara Braden, Dominique Lamarqueet al.

The EDD2020: Endoscopy Disease Detection and Segmentation dataset comprises 380 annotated video frames sourced from various international centers, encompassing different gastrointestinal tract organs and endoscopy modalities. The dataset addresses multiple organ categories, such as the colon, esophagus, and stomach, each linked to specific diseases. EDD2020 includes five classes for both detection and segmentation tasks, with a focus on gastrointestinal organs. Patient data has been anonymized and collected with informed consent, while annotations were performed by clinical experts and post-doctoral researchers, accounting for some variability in annotations due to the subjective nature of interpretation.

Endoscopy is crucial for early cancer detection in organs like the esophagus, stomach, colon, and bladder. Accurate localization and segmentation of diseased regions in clinical endoscopy videos are vital for precise diagnosis and surgical planning. Publicly available datasets for disease detection in endoscopy have been historically limited in scope, often concentrating on specific tasks and imaging modalities.

The authors introduce the EDD2020 challenge, which encompasses multi-class, multi-organ, and multi-population disease detection and segmentation in clinical endoscopy. The challenge evaluates disease region localization using bounding boxes and precise pixel-level segmentation, with a strong emphasis on clinical applicability for real-time monitoring and offline performance assessment. Their network of clinical and computational experts collaboratively gathered, curated, and annotated gastrointestinal endoscopy video frames. Released as part of the EDD2020 challenge, this dataset aims to overcome limitations in disease detection and segmentation. The EDD2020 initiative represents a crowd-sourced effort to assess recent deep learning methods and foster robust technology development.

The dataset in question features the following classes:

  1. BE (Barrett’s Oesophagus)
  2. suspicious
  3. HGD (high-grade dysplasia)
  4. cancer (adenocarcinoma)
  5. polyp
ExpandExpand
Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHubDataset LinkIEEE

Summary #

EDD2020: Endoscopy Disease Detection and Segmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the medical industry.

The dataset consists of 386 images with 1251 labeled objects belonging to 10 different classes including BE_bbox, BE, polyp_bbox, and other: polyp, suspicious_bbox, suspicious, HGD_bbox, HGD, cancer_bbox, and cancer.

Images in the EDD2020 dataset have pixel-level instance segmentation and bounding box annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation task (only one mask for every class). 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 2020 by the UK-IT-FR joint research team.

Dataset Poster

Explore #

EDD2020 dataset has 386 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 EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
OpenSample annotation mask from EDD2020Sample image from EDD2020
👀
Have a look at 386 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 10 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 1-10 of 10
Class
Images
Objects
Count on image
average
Area on image
average
BE_bbox
rectangle
160
286
1.79
66.51%
BE
mask
160
160
1
42.18%
polyp_bbox
rectangle
127
228
1.8
29.34%
polyp
mask
127
127
1
20.2%
suspicious_bbox
rectangle
88
98
1.11
19.08%
suspicious
mask
88
88
1
12.33%
HGD_bbox
rectangle
74
80
1.08
23.15%
HGD
mask
74
74
1
15.17%
cancer_bbox
rectangle
53
57
1.08
30.29%
cancer
mask
53
53
1
19.18%

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 1-10 of 10
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
BE_bbox
rectangle
286
38.11%
99.2%
0.11%
7px
1.97%
511px
99.61%
249px
53.17%
11px
2.14%
512px
99.61%
polyp_bbox
rectangle
228
16.78%
97.75%
0.14%
15px
3.01%
1066px
98.83%
337px
34.05%
20px
2.67%
1356px
99.48%
BE
mask
160
42.18%
93.01%
0.36%
51px
9.94%
511px
99.61%
389px
82.97%
20px
3.89%
512px
99.61%
polyp
mask
127
20.2%
82.64%
0.26%
80px
12.5%
1066px
99.71%
552px
54.7%
72px
6.44%
1356px
99.65%
suspicious_bbox
rectangle
98
17.26%
98.41%
0.54%
50px
10.42%
1035px
99.38%
267px
40.79%
24px
4.67%
983px
99.03%
suspicious
mask
88
12.33%
52.32%
1.2%
50px
10.42%
1035px
99.38%
292px
44.51%
54px
10.51%
983px
99.03%
HGD_bbox
rectangle
80
21.61%
80.9%
0.32%
24px
4.68%
782px
91.81%
300px
44.96%
35px
6.82%
858px
98.05%
HGD
mask
74
15.17%
53.48%
1.49%
65px
13.54%
782px
91.81%
318px
47.48%
67px
13.06%
858px
98.44%
cancer_bbox
rectangle
57
28.61%
93.33%
1.68%
63px
13.12%
880px
98.34%
290px
50.84%
60px
11.67%
947px
96.59%
cancer
mask
53
19.18%
54.25%
1.54%
69px
14.38%
880px
98.8%
299px
51.86%
118px
17.5%
947px
96.59%

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 1251 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 1251
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
BE_bbox
rectangle
EDD2020_B0051.jpeg
480 x 474
471px
98.12%
462px
97.47%
95.64%
2
BE
mask
EDD2020_B0051.jpeg
480 x 474
471px
98.12%
462px
97.47%
85.46%
3
BE_bbox
rectangle
EDD2020_B0065.jpeg
365 x 391
357px
97.81%
385px
98.47%
96.31%
4
BE_bbox
rectangle
EDD2020_B0065.jpeg
365 x 391
148px
40.55%
229px
58.57%
23.75%
5
BE
mask
EDD2020_B0065.jpeg
365 x 391
363px
99.45%
385px
98.47%
47.55%
6
BE_bbox
rectangle
EDD2020_AJ0011.jpeg
480 x 514
471px
98.12%
510px
99.22%
97.36%
7
cancer_bbox
rectangle
EDD2020_AJ0011.jpeg
480 x 514
101px
21.04%
249px
48.44%
10.19%
8
BE
mask
EDD2020_AJ0011.jpeg
480 x 514
471px
98.12%
510px
99.22%
87.16%
9
cancer
mask
EDD2020_AJ0011.jpeg
480 x 514
101px
21.04%
249px
48.44%
8.05%
10
BE_bbox
rectangle
EDD2020_B0067.jpeg
372 x 345
78px
20.97%
104px
30.14%
6.32%

License #

EDD2020: Endoscopy Disease Detection and Segmentation is under CC BY-NC 4.0 license.

Source

Citation #

If you make use of the EDD2020 data, please cite the following reference:

@misc{https://doi.org/10.21227/f8xg-wb80,
  doi = {10.21227/F8XG-WB80},
  url = {https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd2020},
  author = {Ali, Sharib  and Braden, Barbara and Lamarque, Dominique and Realdon, Stefano and Bailey, Adam and Cannizzaro, Renato and Ghatwary, Noha and Rittscher, Jens  and Daul, Christian  and East , James },
  keywords = {Artificial Intelligence, Computer Vision, Image Processing, Machine Learning, Biomedical and Health Sciences, Medical Imaging, Detection, localisation, Semantic Segmentation, out-of-sample detection},
  title = {Endoscopy Disease Detection and Segmentation (EDD2020)},
  publisher = {IEEE DataPort},
  year = {2020},
  copyright = {Creative Commons Attribution}
}

Source

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

@misc{ visualization-tools-for-edd2020-dataset,
  title = { Visualization Tools for EDD2020 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/edd2020 } },
  url = { https://datasetninja.com/edd2020 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset EDD2020 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='EDD2020', 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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