Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

TL;DR

This paper introduces TPNet, a novel neural network that simplifies dental implant depth prediction by framing it as a video grounding task, utilizing texture perception to improve accuracy and efficiency.

Contribution

The paper proposes a new approach that models implant depth prediction as a video grounding problem and introduces a texture perceiving loss for better slice texture analysis.

Findings

TPNet outperforms existing methods in implant depth prediction accuracy.

The texture perceiving loss enhances the encoder's ability to perceive slice textures.

The approach reduces computational resources by cropping implant regions from CBCT data.

Abstract

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods.