KEYWORDS: Networks, X-rays, Anatomy, Education and training, Diseases and disorders, Optical engineering, Medical imaging, Lithium, Integrated circuits, Head
Dental oral disease is one of the most prevalent diseases worldwide, as of a medical analysis in The Lancet 2022[1]. The most common oral diseases worldwide are dental caries (cavities), periodontal disease, tooth loss, and overdevelopment of the jaw caused by excessive unilateral chewing. Dental radiography plays a very important role in clinical diagnosis, treatment and surgery. Automatic segmentation of medical lesions is a prerequisite for efficient clinical analysis. Therefore, accurate positioning of anatomical landmarks is a crucial technique for clinical diagnosis and treatment planning. In this paper, we propose a novel deep network to detect anatomical landmarks. Our proposed network consists of a multi-scale feature aggregation module for channel attention and a deep network for feature refinement. To demonstrate the superiority of our network, training comparisons with several popular networks are performed on the same dataset. The end result is that our network outperforms several popular networks today in both mean radial error (MRE) and successful detection rate (SDR).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.