Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation
This work addresses early diagnosis of dental diseases for medical professionals, but it is incremental as it combines existing methods (BEIT adaptor and Mask2Former) on a new dataset.
The paper tackled the problem of semantic segmentation in dental radiology X-ray images to detect teeth, roots, and various dental diseases, and found that their algorithm, Radious, outperformed state-of-the-art methods by increasing mIoU scores by 9% and 33% compared to Deeplabv3+ and Segformer, respectively.
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.