Dental Clinic X-ray Image Caries Segmentation Dataset
#Image Segmentation #Lesion Detection #Health Status Assessment #Medical Image Analysis #Dental Diagnosis #Intelligent Health Monitoring
- 500 records
- 1.4G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
Currently, dental clinics mainly rely on manual experience for caries detection, posing a risk of missed diagnosis in complex cases and early diagnosis. Many existing automated solutions still lack accuracy and smoothness in image segmentation. The Dental Clinic X-ray Image Caries Segmentation Dataset aims to resolve the technical bottleneck of caries image segmentation, providing extensively annotated data to support precise diagnosis and assistive treatment. Data collection uses standardized X-ray equipment in a professional dental environment to ensure true reflection of diagnostic scenarios. Quality control is conducted through multiple rounds of annotation and consistency checks, performed by a team of 20 individuals with professional dental backgrounds, ensuring annotation accuracy and reliability. Data preprocessing includes noise removal, contrast adjustment, and format conversion, ultimately stored in JPG format for easy access and use.
Dataset Insights
Sample Examples
34d06765**.jpg|1469*864|152.91 KB
b286c172**.jpg|2308*1043|626.21 KB
f4437f1e**.jpg|1744*1078|435.92 KB
62773087**.jpg|1029*571|166.44 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| tooth_number | integer | The identified number of the tooth with cavities, typically between 1 and 32. |
| caries_presence | boolean | An indicator of whether cavities are present in the image. |
| caries_severity | string | The severity of the caries, such as mild, moderate, or severe. |
| caries_location | string | The location of the caries on the tooth, such as occlusal surface, cervical area, or root. |
| image_quality | string | The clarity and contrast quality of the image, such as high, medium, or low. |
| artifact_presence | boolean | An indicator of whether artifacts are present in the image. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What dental applications is this dataset suitable for?
- The dental clinic X-ray image caries segmentation dataset is suitable for research and application development in caries detection and diagnostic accuracy improvement.
- How many X-ray images are included in the dataset?
- The dataset contains multiple X-ray images, and the specific number can be found in the dataset documentation.
- How does this dataset help improve caries detection technology?
- By providing high-quality X-ray images and annotated caries, this dataset can be used for training and testing improved caries detection algorithms.
- What technical knowledge is required to use this dataset?
- Using this dataset for developing automated caries detection typically requires basic knowledge of medical image processing and machine learning.
- How are caries regions labeled in this dataset?
- The dataset usually employs expert dentists to manually label caries regions to ensure accuracy and reliability of the annotations.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Dental Clinic X-ray Image Caries Segmentation Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/b0281fc65788d791cae6eb4e4b9026f2?dataset_scene_cate_type=3},
urldate={2026-02-04},
keywords={Dental X-ray Dataset, Caries Detection, Image Segmentation Data},
version={1.0}
}Using this in research? Please cite us.
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder