Dentalai - Dataset Ninja

Summary #

Dentalai Computer Vision Project is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the medical industry.

The dataset consists of 2495 images with 28904 labeled objects belonging to 4 different classes including tooth, caries, cavity, and other: crack.

Images in the Dentalai dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (1991 images), valid (254 images), and test (250 images). The dataset was released in 2023.

Explore #

Dentalai dataset has 2495 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.

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Have a look at 2495 images

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

Class balance #

There are 4 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 1-4 of 4

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
tooth➔ polygon	2493	22731	9.12	26.38%
caries➔ polygon	1190	4212	3.54	0.91%
cavity➔ polygon	999	1781	1.78	4.55%
crack➔ polygon	154	180	1.17	1.43%

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.

Rows 1-4 of 4

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
tooth polygon	22731	2.85%	87.75%	0%	3px	0.42%	2306px	100%	329px	22.2%	4px	0.33%	2251px	100%
caries polygon	4212	0.24%	62.2%	0%	2px	0.11%	1237px	89.3%	56px	5.25%	2px	0.09%	1107px	99.1%
cavity polygon	1781	2.5%	47.54%	0%	6px	0.82%	1777px	98.85%	176px	19.05%	6px	0.54%	1760px	85.62%
crack polygon	180	1.16%	29.96%	0.01%	6px	2.33%	917px	82.14%	139px	15.74%	4px	0.87%	1120px	63.17%

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 28904 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 28904

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➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	33px	17.01%	31px	11.97%	1.28%
2➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	33px	17.01%	23px	8.88%	1.02%
3➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	31px	15.98%	17px	6.56%	0.41%
4➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	38px	19.59%	29px	11.2%	1.59%
5➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	42px	21.65%	33px	12.74%	1.83%
6➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	27px	13.92%	16px	6.18%	0.6%
7➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	38px	19.59%	38px	14.67%	2.28%
8➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	22px	11.34%	26px	10.04%	0.62%
9➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	35px	18.04%	18px	6.95%	0.88%
10➔	tooth polygon	2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg	194 x 259	28px	14.43%	33px	12.74%	1.37%

License #

Dentalai Computer Vision Project is under CC BY 4.0 license.

Source

Citation #

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

@dataset{Dentalai,
  author={Pawan Valluri},
  title={Dentalai Computer Vision Project},
  year={2023},
  url={https://www.kaggle.com/datasets/pawanvalluri/dental-segmentation}
}

Source

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

@misc{ visualization-tools-for-dentalai-dataset,
  title = { Visualization Tools for Dentalai Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/dentalai } },
  url = { https://datasetninja.com/dentalai },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Dentalai 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='Dentalai', 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.

. . .

Disclaimer #

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