Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation

TL;DR

Radious introduces a novel semantic segmentation method using BEIT adaptor and Mask2Former, significantly improving dental X-ray analysis accuracy for diagnosing various dental conditions.

Contribution

The paper presents a new segmentation algorithm that outperforms existing methods in dental radiology image analysis using BEIT adaptor and Mask2Former.

Findings

Radious achieves 9% higher mIoU than Deeplabv3+.

Radious achieves 33% higher mIoU than Segformer.

The method effectively detects multiple dental abnormalities.

Abstract

X-ray images are the first steps for diagnosing and further treating dental problems. So, early diagnosis prevents the development and increase of oral and dental diseases. In this paper, we developed a semantic segmentation algorithm based on BEIT adaptor and Mask2Former to detect and identify teeth, roots, and multiple dental diseases and abnormalities such as pulp chamber, restoration, endodontics, crown, decay, pin, composite, bridge, pulpitis, orthodontics, radicular cyst, periapical cyst, cyst, implant, and bone graft material in panoramic, periapical, and bitewing X-ray images. We compared the result of our algorithm to two state-of-the-art algorithms in image segmentation named: Deeplabv3 and Segformer on our own data set. We discovered that Radious outperformed those algorithms by increasing the mIoU scores by 9% and 33% in Deeplabv3+ and Segformer, respectively.