文献类型： 会议论文
第一作者： Yang Xing
作者： Yang Xing 1 ; Peixi Liao 2 ; Reem AwdhE Alasleh 2 ; Vissuta Khampatee 2 ; Farshid Alizadeh-Shabdiz 1 ;
作者机构： 1.Computer Science Department, Boston University Metropolitan College, Boston, United States
2.Department of Restorative Sciences and Biomaterials, Boston University Henry M Goldman School of Dental Medicine, Boston, United States
关键词： Image segmentation;Accuracy;Implants;Teeth;X-rays;Planning;Dentistry;Convolutional neural networks;Diagnostic radiography;X-ray imaging
会议名称： IEEE Internaitonal Conference on Multimedia Information Processing and Retrieval
主办单位：
页码： 243-246
摘要： Panoramic radiographs serve as valuable resources in terms of dental diagnostics and implant planning and require dedicated and time-consuming analysis. Lots of studies applying panoramic radiographs on segmentation tasks using deep learning methods have been conducted. However, few works consider the post-processing part after getting the output of the segmentation. This paper introduces a pipeline that provides end-to-end application of segmenting tooth and other oral structures from X-rays and uses an edge detection algorithm to extract the contours of the tooth and use PCA and linear regression to predict implant planning locations based on the contours. The methodology presented in this article entails the development of a CNN model based on ResUNet designed for segmentation tasks from both panoramic and tangential oral images. These segmented images including teeth, restorations, and other oral structures are used to locate two adjacent teeth that contain a missing area where an implant is suggested. Then based on the main axes of these two adjacent teeth, the main axis of a potential implant is predicted. The model's performance is evaluated using Intersection over Union (IoU) and pixel accuracy metrics, while the precision of implant placement is gauged by measuring distances and angles relative to actual implant locations. The dataset, consisting of images from 100 patients, illustrates the model's high efficacy, achieving an accuracy rate of 0.9785 and an IoU of 0.8483. The predicted implant locations exhibit an average deviation of 0.360 mm and a maximum deviation of 0.795 mm from those determined by dental professionals.
分类号： tp391-53
- 相关文献
[1]Enhancing X-ray Image Quality for Improved Medical Diagnosis: A Novel Filtering Algorithm. Miguel Bustamante,Antonio Rienzo,Gastón Lefranc. 2024
[2]Cascaded coarse-to-fine convolutional neural networks for pericardial effusion localization and segmentation on CT scans. Jiamin Liu,Jinzheng Cai,Karthik Chellamuthu,Mohammadhadi Bagheri,Le Lu,Ronald M. Summers. 2018
[3]Automatic Multi-Atlas Liver Segmentation and Couinaud Classification from CT Volumes. Sofia Pla-Alemany,Juan Antonio Romero,Jose Manuel Santabarbara,Roberto Aliaga,Alicia M. Maceira,David Moratal. 2021
[4]Extraction of Settlements in Plains and Mountainous Areas Based on High-Resolution Remote Sensing Data and SAM Segmentation Algorithm. Miao Liu,Shijun Wang,Zhenhai Li,Jing Chen. 2024
[5]3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images. Ji, Shunping,Zhang, Chi,Xu, Anjian,Xu, Anjian,Shi, Yun,Duan, Yulin. 2018
[6]Online Measurement of Content in Block Frozen Shrimp by Fusion of X-ray Imaging and Binocular Vision. Sun, Li,Cai, Jian-rong,Lv, Song,Ye, Chuang,Liu, Bin,Sun, Li,Cai, Jian-rong.
[7]Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network. Cai, N,Hu, KH,Xiong, HT,Li, SY,Su, WF,Zhu, FS.
[8]Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm. Zhang, Jianhua,Kong, Fantao,Wu, Jianzhai,Han, Shuqing,Zhai, Zhifen. 2018
[9]Image Segmentation to HSI Model Based on Improved Particle Swarm Optimization. Zhao, Bo,Mao, Wenhua,Zhang, Xiaochao,Chen, Yajun. 2009
[10]A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Ma, Juncheng,Du, Keming,Zheng, Feixiang,Chu, Jinxiang,Sun, Zhongfu,Zhang, Lingxian.
[11]A comparative analysis of five global cropland datasets in China. Lu, Miao,Wu, WenBin,Zhang, Li,Tang, HuaJun,Liao, AnPing,Peng, Shu. 2016
[12]Assess the Accuracy of the Globcover Cultivated Lan d Data in Northeast China. Zhang, Li,Wu, Wenbin,Zhou, Qingbo,Chen, Zhongxin,Li, Zhengguo,Wu, Wenbin,Zhou, Qingbo,Chen, Zhongxin,Li, Zhengguo. 2012
[13]Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties. Tao, FL,Yokozawa, M,Zhang, Z,Xu, YL,Hayashi, Y.
[14]Development of Synthetic Barrier Dental Membrane for Guided Bone Regeneration. Ozan U?ur,Ozan Karaman. 2018
[15]Design of Injectable Pre-Mixed Dental Putty for Bone Regeneration Applications in Dentistry. Keremcan Kuru,Hüseyin Ak?ay,Ozan Karaman. 2018
[16]Detection of Degenerative Osteophytes of the Spine on PET/CT Using Region-Based Convolutional Neural Networks. Yinong Wang,Jianhua Yao,Joseph E. Burns,Jiamin Liu,Ronald M. Summers. 2017
[17]W-net: Deep Convolutional Network with Gray-Level Co-occurrence Matrix and Hybrid Loss Function for Hyperspectral Image Classification. Jinchao Jiao,Changqing Yin,Fei Teng. 2023
[18]Learning with Fixed Point Condition for Convergent PNP PET Reconstruction. Marion Savanier,Claude Comtat,Florent Sureau. 2024
[19]Automated Fruit Classification Based on Deep Learning Utilizing Yolov8. Christine Dewi,Oksam M Kamlasi,Gunjan Chhabra,Guowei Dai,Keshav Kaushik,Inam Ullah Khan. 2023
[20]A coarse-to-fine approach for pericardial effusion localization and segmentation in chest CT scans. Jiamin Liu,Karthik Chellamuthu,Le Lu,Mohammadhadi Bagheri,Ronald M. Summers. 2018
