bibtext: @InProceedings{ Yan_Simplify_MICCAI2024,
                   author = { Yang, Xinquan and Li, Xuguang and Luo, Xiaoling and Zeng, Leilei and Zhang, Yudi and Shen, Linlin and Deng, Yongqiang },
                   title = { { Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
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 network has 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 Perceiver Implant Depth Prediction Network (TPNet), which enables us to directly output the imaplant 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.
title: Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network
authors: Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang