OBJECTIVE: To estimate a patient-specific reference bone shape model for a patient with craniomaxillofacial (CMF) defects due to facial trauma. METHODS: We proposed an automatic facial bone shape estimation framework using pre-traumatic conventional portrait photos and post-traumatic head computed tomography (CT) scans via a 3D face reconstruction and a deformable shape model. Specifically, a three-dimensional (3D) face was first reconstructed from the patient's pre-traumatic portrait photos. Second, a correlation model between the skin and bone surfaces was constructed using a sparse representation based on the CT images of training normal subjects. Third, by feeding the reconstructed 3D face into the correlation model, an initial reference shape model was generated. In addition, we refined the initial estimation by applying non-rigid surface matching between the initially estimated shape and the patient's post-traumatic bone based on the adaptive-focus deformable shape model (AFDSM). Furthermore, a statistical shape model, built from the training normal subjects, was utilized to constrain the deformation process to avoid overfitting. RESULTS AND CONCLUSION: The proposed method was evaluated using both synthetic and real patient data. Experimental results show that the patient's abnormal facial bony structure can be recovered using our method, and the estimated reference shape model is considered clinically acceptable by an experienced CMF surgeon. SIGNIFICANCE: The proposed method is more suitable to the complex CMF defects for CMF reconstructive surgical planning.
OBJECTIVE: To estimate a patient-specific reference bone shape model for a patient with craniomaxillofacial (CMF) defects due to facial trauma. METHODS: We proposed an automatic facial bone shape estimation framework using pre-traumatic conventional portrait photos and post-traumatic head computed tomography (CT) scans via a 3D face reconstruction and a deformable shape model. Specifically, a three-dimensional (3D) face was first reconstructed from the patient's pre-traumatic portrait photos. Second, a correlation model between the skin and bone surfaces was constructed using a sparse representation based on the CT images of training normal subjects. Third, by feeding the reconstructed 3D face into the correlation model, an initial reference shape model was generated. In addition, we refined the initial estimation by applying non-rigid surface matching between the initially estimated shape and the patient's post-traumatic bone based on the adaptive-focus deformable shape model (AFDSM). Furthermore, a statistical shape model, built from the training normal subjects, was utilized to constrain the deformation process to avoid overfitting. RESULTS AND CONCLUSION: The proposed method was evaluated using both synthetic and real patient data. Experimental results show that the patient's abnormal facial bony structure can be recovered using our method, and the estimated reference shape model is considered clinically acceptable by an experienced CMF surgeon. SIGNIFICANCE: The proposed method is more suitable to the complex CMF defects for CMF reconstructive surgical planning.
Authors: James J Xia; Carl V Phillips; Jaime Gateno; John F Teichgraeber; Andrew M Christensen; Michael J Gliddon; Jeremy J Lemoine; Michael A K Liebschner Journal: J Oral Maxillofac Surg Date: 2006-12 Impact factor: 1.895
Authors: Wiebke Semper-Hogg; Marc Anton Fuessinger; Steffen Schwarz; Edward Ellis; Carl-Peter Cornelius; Florian Probst; Marc Christian Metzger; Stefan Schlager Journal: J Craniomaxillofac Surg Date: 2016-12-24 Impact factor: 2.078
Authors: James J Xia; Liza Shevchenko; Jaime Gateno; John F Teichgraeber; Terry D Taylor; Robert E Lasky; Jeryl D English; Chung H Kau; Kathleen R McGrory Journal: J Oral Maxillofac Surg Date: 2011-07 Impact factor: 1.895
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