Deqiang Xiao1, Hannah Deng2, Chunfeng Lian1, Tianshu Kuang2, Qin Liu1, Lei Ma1, Yankun Lang1, Xu Chen1, Daeseung Kim2, Jaime Gateno2,3, Steve Guofang Shen4, Dinggang Shen1, Pew-Thian Yap1, James J Xia2,3. 1. Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 2. Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, Houston, Texas, USA. 3. Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, USA. 4. Shanghai Ninth Hospital, Shanghai Jiaotong University College of Medicine, Shanghai, China.
Abstract
PURPOSE: The purpose of this study was to reduce the experience dependence during the orthognathic surgical planning that involves virtually simulating the corrective procedure for jaw deformities. METHODS: We introduce a geometric deep learning framework for generating reference facial bone shape models for objective guidance in surgical planning. First, we propose a surface deformation network to warp a patient's deformed bone to a set of normal bones for generating a dictionary of patient-specific normal bony shapes. Subsequently, sparse representation learning is employed to estimate a reference shape model based on the dictionary. RESULTS: We evaluated our method on a clinical dataset containing 24 patients, and compared it with a state-of-the-art method that relies on landmark-based sparse representation. Our method yields significantly higher accuracy than the competing method for estimating normal jaws and maintains the midfaces of patients' facial bones as well as the conventional way. CONCLUSIONS: Experimental results indicate that our method generates accurate shape models that meet clinical standards.
PURPOSE: The purpose of this study was to reduce the experience dependence during the orthognathic surgical planning that involves virtually simulating the corrective procedure for jaw deformities. METHODS: We introduce a geometric deep learning framework for generating reference facial bone shape models for objective guidance in surgical planning. First, we propose a surface deformation network to warp a patient's deformed bone to a set of normal bones for generating a dictionary of patient-specific normal bony shapes. Subsequently, sparse representation learning is employed to estimate a reference shape model based on the dictionary. RESULTS: We evaluated our method on a clinical dataset containing 24 patients, and compared it with a state-of-the-art method that relies on landmark-based sparse representation. Our method yields significantly higher accuracy than the competing method for estimating normal jaws and maintains the midfaces of patients' facial bones as well as the conventional way. CONCLUSIONS: Experimental results indicate that our method generates accurate shape models that meet clinical standards.
Authors: Li Wang; Yi Ren; Yaozong Gao; Zhen Tang; Ken-Chung Chen; Jianfu Li; Steve G F Shen; Jin Yan; Philip K M Lee; Ben Chow; James J Xia; Dinggang Shen Journal: Med Phys Date: 2015-10 Impact factor: 4.071
Authors: Sam Sheng-Pin Hsu; Jaime Gateno; R Bryan Bell; David L Hirsch; Michael R Markiewicz; John F Teichgraeber; Xiaobo Zhou; James J Xia Journal: J Oral Maxillofac Surg Date: 2012-06-12 Impact factor: 1.895
Authors: J J Xia; J Gateno; J F Teichgraeber; P Yuan; K-C Chen; J Li; X Zhang; Z Tang; D M Alfi Journal: Int J Oral Maxillofac Surg Date: 2015-12 Impact factor: 2.789
Authors: J J Xia; J Gateno; J F Teichgraeber; P Yuan; J Li; K-C Chen; A Jajoo; M Nicol; D M Alfi Journal: Int J Oral Maxillofac Surg Date: 2015-12 Impact factor: 2.789