TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental
  Implant Position Prediction
Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng
In implant prosthesis treatment, the surgical guide of implant is used to
ensure accurate implantation. However, such design heavily relies on the manual
location of the implant position. When deep neural network has been proposed to
assist the dentist in locating the implant position, most of them take a single
slice as input, which do not fully explore 3D contextual information and
ignoring the influence of implant slope. In this paper, we design a Text Guided
3D Context and Slope Aware Triple Network (TCSloT) which enables the perception
of contextual information from multiple adjacent slices and awareness of
variation of implant slopes. A Texture Variation Perception (TVP) module is
correspondingly elaborated to process the multiple slices and capture the
texture variation among slices and a Slope-Aware Loss (SAL) is proposed to
dynamically assign varying weights for the regression head. Additionally, we
design a conditional text guidance (CTG) module to integrate the text condition
(i.e., left, middle and right) from the CLIP for assisting the implant position
prediction. Extensive experiments on a dental implant dataset through five-fold
cross-validation demonstrated that the proposed TCSloT achieves superior
performance than existing methods.