Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and
  Mask2Former in Semantic Segmentation
Mohammad Mashayekhi Sara Ahmadi Majd Arian Amiramjadi Babak Mashayekhi
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.