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

TL;DR

This paper introduces TCSloT, a neural network that leverages 3D context, slope awareness, and text guidance to improve dental implant position prediction, outperforming existing methods.

Contribution

The paper presents a novel 3D-aware neural network with texture variation perception, slope-aware loss, and text guidance modules for more accurate implant positioning.

Findings

TCSloT outperforms existing methods in implant position prediction.

The texture variation perception module effectively captures 3D contextual information.

The slope-aware loss improves regression accuracy.

Abstract

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.