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In implant prosthesis treatment, the surgical guide of implant is used toensure accurate implantation. However, such design heavily relies on the manuallocation of the implant position. When deep neural network has been proposed toassist the dentist in locating the implant position, most of them take a singleslice as input, which do not fully explore 3D contextual information andignoring the influence of implant slope. In this paper, we design a Text Guided3D Context and Slope Aware Triple Network (TCSloT) which enables the perceptionof contextual information from multiple adjacent slices and awareness ofvariation of implant slopes. A Texture Variation Perception (TVP) module iscorrespondingly elaborated to process the multiple slices and capture thetexture variation among slices and a Slope-Aware Loss (SAL) is proposed todynamically assign varying weights for the regression head. Additionally, wedesign a conditional text guidance (CTG) module to integrate the text condition(i.e., left, middle and right) from the CLIP for assisting the implant positionprediction. Extensive experiments on a dental implant dataset through five-foldcross-validation demonstrated that the proposed TCSloT achieves superiorperformance than existing methods.

TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction