Advancing Wound Filling Extraction on 3D Faces:An Auto-Segmentation and Wound Face Regeneration Approach

作     者：Duong Q.Nguyen Thinh D.Le Phuong D.Nguyen Nga T.K.Le H.Nguyen-Xuan 

作者机构：Department of Mathematics and StatisticsQuy Nhon UniversityQuy Nhon City55100Viet Nam Applied Research Institute for Science and TechnologyQuy Nhon UniversityQuy Nhon City55100Viet Nam CIRTECH InstituteHUTECH UniversityHo Chi Minh City72308Viet Nam 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第139卷第5期

页      面：2197-2214页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Vietnam Institute for Advanced Study in Mathematics  VIASM 

主　　题：3D printing technology face reconstruction 3D segmentation 3D printed model 

摘      要：Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical *** this paper,we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional *** method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss *** achieve accurate segmentation,we conducted thorough experiments and selected a high-performing model from the *** selectedmodel demonstrates exceptional segmentation performance for complex 3D facial ***,based on the segmentation model,we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous *** method achieved a remarkable accuracy of 0.9999993% on the test suite,surpassing the performance of the previous *** this result,we use 3D printing technology to illustrate the shape of the wound *** outcomes of this study have significant implications for physicians involved in preoperative planning and intervention *** automating facial wound segmentation and improving the accuracy ofwound-filling extraction,our approach can assist in carefully assessing and optimizing interventions,leading to enhanced patient ***,it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue *** source code is available at https://***/SIMOGroup/WoundFilling3D.