@inproceedings{d3aaa7b4ac7546e9ae791be55396171e,
title = "Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network",
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.",
keywords = "Deep Learning, Dental Implant, Implant Depth Prediction",
author = "Xinquan Yang and Xuguang Li and Xiaoling Luo and Leilei Zeng and Yudi Zhang and Linlin Shen and Yongqiang Deng",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 ; Conference date: 06-10-2024 Through 10-10-2024",
year = "2024",
doi = "10.1007/978-3-031-72086-4_57",
language = "English",
isbn = "9783031720857",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "606--615",
editor = "Linguraru, {Marius George} and Qi Dou and Aasa Feragen and Stamatia Giannarou and Ben Glocker and Karim Lekadir and Schnabel, {Julia A.}",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings",
address = "Germany",
}