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Streamlining dental analysis by automatically segmenting teeth in 3D intra-oral scans

See how this company used accurate point cloud segmentation annotations to automate dental analysis

 

Challenge

Intra-oral point cloud scans are highly detailed, often containing millions of points in each point cloud. The company needed to accurately label different structures within these point clouds—such as teeth, gums, and dental restorations—while preserving the subtle details that are crucial for dental applications. But the sheer density and complexity of this data presented a significant challenge for annotation.

Solution

The company leveraged Mindkosh's annotation platform to address these challenges. Mindkosh is designed to handle high-density RGB point clouds, offering advanced segmentation tools to allow annotators to accurately visualize, interact and label them. They utilized a Honeypot system to accurately measure the quality of the annotated data, and used the in-built workflows to maintain a tight Quality Control process.

Outcome

This resulted in a significant improvement in both the efficiency and accuracy of the annotation process. The company was able to label their intra-oral scans more quickly, reducing the time required to prepare training data by approximately 30%. More importantly, the annotations were highly accurate, enabling them to develop smarter, more reliable tools for dental professionals, improving the overall quality of digital dental care.

A dental technology company sought to develop advanced AI models capable of analyzing high-density RGB intra-oral point cloud scans. These scans are essential for creating accurate digital representations of the mouth, which are used in everything from orthodontic treatment planning to the creation of custom dental prosthetics. The company's goal was to train models that could automatically identify and segment various dental structures within these dense point clouds, enabling more efficient and precise dental care.
Intra-oral point cloud scans are highly complex, with millions of points accurately capturing the surfaces and textures of teeth and soft tissues. This high density and complexity presented a significant challenge for annotation. The company needed to accurately label different structures within these point clouds—such as teeth, gums, and dental restorations—while preserving the subtle details that are crucial for dental applications. Traditional annotation methods struggled with this level of detail, leading to inconsistent labels and inefficient workflows.
The company leveraged Mindkosh's point cloud annotation platform to address these challenges. Mindkosh is designed to handle high-density RGB point clouds, offering advanced segmentation tools to allow annotators to accurately visualize, interact and label them. They further utilized a Honeypot system to accurately measure the quality of the annotated data, and used the in-build workflow to maintain a tight Quality Control process.
Labeling 3D point clouds from intra-oral dental scans
The adoption of our point cloud annotation platform resulted in a significant improvement in both the efficiency and accuracy of the annotation process. The company was able to label their intra-oral scans more quickly, reducing the time required to prepare training data by approximately 30%. More importantly, the annotations were highly accurate, preserving the detailed information needed for precise model training.
These well-labeled point clouds enabled the company's AI models to achieve higher performance in identifying and segmenting dental structures. As a result, the company was able to develop more reliable and effective tools for dental professionals, improving the overall quality of digital dental care.