language:en language:ar size_categories:1K<n<10K format:imagefolder modality:image library:datasets library:mlcroissant region:us biology dental AI teeth medical
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language:
- en
- ar
tags:
- biology
- dental
- AI
- teeth
- medical
size_categories:
- n<1K
---

# Panoramic X-ray Tooth Dataset

## Description
This dataset, curated by Humans in the Loop, features over 500 panoramic dental X-ray images, complete with segmentation masks and annotation files. Designed to support dental research, machine learning model development, and automated dental diagnostics, the dataset showcases a variety of dental conditions and tooth structures. The high-quality segmentation masks, produced through expert human annotation, enhance the precision and reliability of model training. This dataset is available on Kaggle through Humans in the Loop’s official page, reflecting their commitment to providing accurate, well-annotated data for AI applications.This dataset, curated by Humans in the Loop, features over 500 panoramic dental X-ray images, complete with segmentation masks and annotation files. Designed to support dental research, machine learning model development, and automated dental diagnostics, the dataset showcases a variety of dental conditions and tooth structures. The high-quality segmentation masks, produced through expert human annotation, enhance the precision and reliability of model training. This dataset is available on Kaggle through Humans in the Loop’s official page, reflecting their commitment to providing accurate, well-annotated data for AI applications.
https://www.kaggle.com/datasets/humansintheloop/
## Dataset Structure
The dataset includes the following components:

1. **X-ray Images**: High-resolution panoramic X-ray images in JPG format.
2. **Machine Masks**: Segmentation masks automatically generated by machine learning models, representing the detected teeth and surrounding structures.
3. **Human Masks**: Segmentation masks manually annotated by dental professionals, serving as ground truth.
4. **JSON Annotation Files**: Each X-ray image is accompanied by a JSON file containing polygon coordinates outlining the exterior boundaries of each tooth, along with class labels.

### Example Files

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/66b24a579f3dfa895ca9286d/aT6K4yq6MRQOuAXqYf3SB.jpeg)

Example panoramic X-ray image.This image is a standard panoramic X-ray, capturing the full set of teeth, jaw, and surrounding bone structure in a single image. The process involves using a rotating arm of an X-ray machine that captures the dental arch. This type of imaging is widely used for dental diagnosis, as it provides a comprehensive view of the mouth.This image can be used to detect dental anomalies, evaluate tooth alignment, and check for issues such as impacted teeth or bone loss.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/66b24a579f3dfa895ca9286d/3m_g2O9Bo7sGAwKsV0uvN.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66b24a579f3dfa895ca9286d/qklFYtxQXpq8jqxdcIpjC.png)

Corresponding machine or human mask.The mask highlights individual teeth by segmenting them from the X-ray image.In the case of machine-generated masks, computer vision models have been trained to detect and separate each tooth, while human masks are manually drawn for accuracy.Overlay the mask on the X-ray to visualize segmentation. This is useful for training machine learning models to improve automatic segmentation accuracy.

Annotation file containing labeled polygon data for each tooth.This JSON file contains detailed polygon coordinates that map out the boundaries of each tooth detected in the X-ray. Each entry in the file corresponds to a specific tooth number or region, providing crucial information for dental diagnostics and AI model training.The polygon data can be used to train segmentation models, fine-tune existing algorithms, or serve as ground truth for evaluating model performance.

 


## Usage
This dataset can be used to train segmentation models for tooth identification, dental health assessment, and automated diagnostics. The machine-generated masks can help in benchmarking, while human-annotated masks provide reliable ground truth for comparison.