Dataset Ninja LogoDataset Ninja:

CVC-ClinicDB Dataset

61212
Tagmedical
Tasksemantic segmentation
Release YearMade in 2015
Licensecustom
Download50 MB

Introduction #

Jorge Bernal, F. Javier Sánchez, Gloria Fernández-Esparrachet al.

The CVC-ClinicDB dataset includes 612 frames taken from colonoscopy videos, which feature numerous instances of polyp. Accompanying these frames, the ground truth for the polyps is also provided. The ground truth is represented by a mask that corresponds to the area of the image occupied by the polyp.

CVC-ClinicDB has been generated from 25 different video studies. For each study at least a sequence containing a polyp was extracted. Considering this, CVC-ClinicDB database is composed by frames from 29 different sequences containing a polyp.

Finally, a set of frames were selected from each sequence, paying particular attention in showing several points of view of the polyp.

The correspondence between the number of frame and the video sequence is as follows:

Frame Number Sequence
1 to 25 1
26 to 50 2
51 to 67 3
68 to 78 4
79 to 103 5
104 to 126 6
127 to 151 7
152 to 177 8
178 to 199 9
200 to 205 10
206 to 227 11
228 to 252 12
253 to 277 13
278 to 297 14
298 to 317 15
318 to 342 16
343 to 363 17
364 to 383 18
384 to 408 19
409 to 428 20
429 to 447 21
448 to 466 22
467 to 478 23
479 to 503 24
504 to 528 25
529 to 546 26
547 to 571 27
572 to 591 28
592 to 612 29

CVC-ClinicDB is the database to be used in the training stages of ISBI 2015 Challenge on Automatic Polyp Detection Challenge in Colonoscopy Videos.

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Dataset LinkHomepageDataset LinkResearch Paper 1 (main)Dataset LinkResearch Paper 2Dataset LinkKaggle

Summary #

CVC-ClinicDB is a dataset for a semantic segmentation task. It is used in the medical industry.

The dataset consists of 612 images with 612 labeled objects belonging to 1 single class (polyp).

Images in the CVC-ClinicDB 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. Additionally, every image was recorded with its own video sequence. The dataset was released in 2015 by the CVC Computer Vision Center, UAB, Spain and Hospital Clinic Barcelona, Spain.

Dataset Poster

Explore #

CVC-ClinicDB dataset has 612 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 CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
OpenSample annotation mask from CVC-ClinicDBSample image from CVC-ClinicDB
👀
Have a look at 612 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
polyp
mask
612
612
1
9.17%

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
polyp
mask
612
9.17%
48.96%
0.34%
22px
7.64%
270px
93.75%
114px
39.67%
23px
5.99%
313px
81.51%

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 612 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 612
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
polyp
mask
182.png
288 x 384
60px
20.83%
74px
19.27%
3.17%
2
polyp
mask
250.png
288 x 384
186px
64.58%
74px
19.27%
9.2%
3
polyp
mask
361.png
288 x 384
123px
42.71%
138px
35.94%
9.95%
4
polyp
mask
153.png
288 x 384
71px
24.65%
115px
29.95%
5.71%
5
polyp
mask
465.png
288 x 384
127px
44.1%
131px
34.11%
10.9%
6
polyp
mask
372.png
288 x 384
34px
11.81%
41px
10.68%
0.91%
7
polyp
mask
111.png
288 x 384
242px
84.03%
138px
35.94%
23.17%
8
polyp
mask
54.png
288 x 384
25px
8.68%
25px
6.51%
0.4%
9
polyp
mask
322.png
288 x 384
89px
30.9%
116px
30.21%
7.3%
10
polyp
mask
140.png
288 x 384
156px
54.17%
187px
48.7%
20.62%

License #

Copyright

The original images (i.e. the Images from folder ‘Original’) are property of Hospital Clinic, Barcelona, Spain

The annotations (i.e. Images from folder ‘Ground Truth’) are property of Computer Vision Center, Barcelona, Spain

Referencing

The use of this database is completely restricted for research and educational purposes. The use of this database is forbidden for commercial purposes.

If you use this database for your experiments please include the following reference:

Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015). WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics, 43, 99-111.

Source

Citation #

If you make use of the CVC-ClinicDB data, please cite the following reference:

@dataset{CVC-ClinicDB,
  author={Jorge Bernal and F. Javier Sánchez and Gloria Fernández-Esparrach and Debora Gil and Cristina Rodríguez and Fernando Vilariño},
  title={CVC-ClinicDB},
  year={2015},
  url={https://polyp.grand-challenge.org/CVCClinicDB/}
}

Source

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

@misc{ visualization-tools-for-cvc-dataset,
  title = { Visualization Tools for CVC-ClinicDB Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/cvc-612 } },
  url = { https://datasetninja.com/cvc-612 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset CVC-ClinicDB 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='CVC-ClinicDB', 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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