Teeth Segmentation on Dental X-ray Images

Introduction #

The Teeth Segmentation dataset comprises 598 images, and it involves 32 classes denoted as 1, 2, 3, 4, and so on up to 32. The numbering sequence initiates from the upper left teeth and proceeds in a clockwise direction, encompassing the lower left teeth. Every tooth is assigned a distinct number corresponding to its specific location.

Homepage

Summary #

Teeth Segmentation on Dental X-ray Images is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the medical industry.

The dataset consists of 598 images with 15021 labeled objects belonging to 32 different classes including 23, 25, 26, and other: 24, 27, 22, 28, 21, 11, 8, 9, 6, 10, 7, 15, 20, 29, 2, 12, 3, 5, 14, 4, 31, 13, 18, 19, 30, 1, 17, 16, and 32.

Images in the Teeth Segmentation on Dental X-ray Images dataset have pixel-level semantic segmentation annotations. There are 3 (1% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2023 by the Humans In The Loop, Bulgaria.

Explore #

Teeth Segmentation on Dental X-ray Images dataset has 598 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.

Sample annotation mask from Teeth Segmentation on Dental X-ray Images

Sample image from Teeth Segmentation on Dental X-ray Images

👀

Have a look at 598 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 32 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.

Rows 1-10 of 32

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
23➔ mask	582	582	1	0.39%
26➔ mask	580	580	1	0.4%
25➔ mask	580	580	1	0.34%
24➔ mask	575	575	1	0.33%
27➔ mask	574	574	1	0.57%
22➔ mask	572	572	1	0.55%
28➔ mask	542	542	1	0.51%
21➔ mask	531	531	1	0.48%
11➔ mask	516	516	1	0.6%
9➔ mask	514	514	1	0.68%

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.

Rows 1-10 of 32

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
23 mask	582	0.39%	0.76%	0.13%	88px	8.59%	277px	27.05%	188px	18.35%	36px	1.76%	195px	10.53%
26 mask	580	0.4%	0.92%	0.2%	117px	11.43%	298px	29.1%	186px	18.2%	38px	1.86%	133px	7.02%
25 mask	580	0.34%	0.88%	0.14%	88px	8.59%	290px	28.32%	174px	17%	30px	1.47%	114px	6.1%
24 mask	575	0.33%	0.74%	0.16%	105px	10.25%	287px	28.03%	174px	16.99%	36px	1.76%	118px	5.78%
27 mask	574	0.57%	1.24%	0.21%	77px	7.52%	317px	30.96%	218px	21.26%	39px	1.91%	238px	11.66%
22 mask	572	0.55%	1.04%	0.13%	56px	5.47%	309px	30.18%	220px	21.53%	38px	1.86%	186px	9.11%
28 mask	542	0.51%	1.18%	0.17%	79px	7.71%	290px	28.32%	207px	20.2%	51px	2.5%	199px	9.75%
21 mask	531	0.48%	0.98%	0.13%	70px	6.84%	280px	27.34%	208px	20.27%	37px	1.81%	194px	10.49%
11 mask	516	0.6%	1.17%	0.24%	98px	9.57%	544px	53.12%	242px	23.66%	51px	2.5%	1147px	56.2%
9 mask	514	0.68%	1.68%	0.14%	75px	7.32%	319px	31.15%	230px	22.47%	48px	2.35%	192px	12.05%

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.

Objects #

Table contains all 15021 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.

Rows 1-10 of 15021

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	13 mask	296.jpg	1024 x 2041	239px	23.34%	67px	3.28%	0.58%
2➔	14 mask	296.jpg	1024 x 2041	225px	21.97%	98px	4.8%	0.75%
3➔	15 mask	296.jpg	1024 x 2041	228px	22.27%	98px	4.8%	0.81%
4➔	11 mask	296.jpg	1024 x 2041	270px	26.37%	70px	3.43%	0.7%
5➔	12 mask	296.jpg	1024 x 2041	246px	24.02%	59px	2.89%	0.51%
6➔	21 mask	296.jpg	1024 x 2041	215px	21%	84px	4.12%	0.51%
7➔	22 mask	296.jpg	1024 x 2041	233px	22.75%	79px	3.87%	0.55%
8➔	23 mask	296.jpg	1024 x 2041	206px	20.12%	56px	2.74%	0.41%
9➔	24 mask	296.jpg	1024 x 2041	181px	17.68%	54px	2.65%	0.34%
10➔	25 mask	296.jpg	1024 x 2041	193px	18.85%	56px	2.74%	0.37%

License #

Teeth Segmentation on Dental X-ray Images is under CC0 1.0 license.

Source

Citation #

If you make use of the Teeth Segmentation on Dental X-ray Images data, please cite the following reference:

 @misc{humans in the loop_2023,
	title={Teeth Segmentation on dental X-ray images},
	url={https://www.kaggle.com/dsv/5884500},
	DOI={10.34740/KAGGLE/DSV/5884500},
	publisher={Kaggle},
	author={Humans In The Loop},
	year={2023}
}

Source

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

@misc{ visualization-tools-for-teeth-segmentation-dataset,
  title = { Visualization Tools for Teeth Segmentation on Dental X-ray Images Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/teeth-segmentation } },
  url = { https://datasetninja.com/teeth-segmentation },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { may },
  note = { visited on 2024-05-16 },
}

Download #

Dataset Teeth Segmentation on Dental X-ray Images 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='Teeth Segmentation on Dental X-ray Images', 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.

. . .

Disclaimer #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.