Estimating patient‐specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation

Wang, Li; Ren, Yi; Gao, Yaozong; Tang, Zhen; Chen, Ken Chung; Li, Jianfu; Shen, Steve Guofang; Jin, Yan; Lee, Philip K.M.; Chow, Ben; Xia, James J.; Shen, Dinggang

doi:10.1118/1.4929974

Cited by 20 publications

(33 citation statements)

References 44 publications

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“…To alleviate the limitation of Wang et al's method [15], we propose an upgraded sparse representation-based patientspecific reference CMF shape prediction method, which is motivated by canonical correlation analysis (CCA) [18]- [21] and multiple dictionary learning [25]- [27]. The flowchart of the proposed method is given in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…To our knowledge, there is only one related work reported in [15], in which SR technique was used to predict the reference jaw shape using normal cranial base and midface. The bony landmarks are grouped into two parts: the midface landmark part including the landmarks of both cranial base and midface (red points in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the predicted reference jaw shape and the normal midface shape of the patient were combined to derive the whole patient-specific reference CMF shape. The prediction accuracy highly depends on the correlation between midface and jaw landmarks in the method developed by Wang et al [15]. However, in practice, the correlation between midface and jaw landmarks may not be strong, which would result in unsatisfactory prediction results.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the measurements are subjective and highly dependent on the surgeon's clinical experience. The surgical planning would be more objective and personalized if the surgeon preoperatively knows what the patient's normal CMF shape should be [15]. Therefore, predicting patient's normal (reference) CMF shape is of critical importance during orthognathic surgical planning.…”

Section: Introductionmentioning

confidence: 99%

“…To predict the patient-specific reference shape, Wang et al [15] proposed a sparse representation (SR) based reference CMF shape prediction method, in which the patient's normal cranial base and midface were used to predict reference jaw shape using the landmarks [16], [17] on midface and jaw shape, as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

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Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space

Teng

Cheng

et al. 2020

IEEE Access

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Patients with Craniomaxillofacial (CMF) deformities always get isolated deformed jaws with normal midfaces. Orthognathic surgery is specifically designed to correct jaws of the patients with normal midfaces. To improve performance of the surgery, a patient-specific normal reference jaw shape is necessary to guide surgical planning prior to the treatment. Sparse representation (SR) technique can be used to predict the reference jaw shape with guidance from the jaws of normal subjects with similar midfaces as the patient's midface. Current SR-based prediction method implicitly assumed that patient's midface shape was strongly correlated with his/her jaw shape. Unfortunately, the assumption is difficult to meet in practice due to different data distributions of midface and jaw. To alleviate this issue, we propose a patient-specific reference jaw shape prediction method via sparse representation in a coherent space mapped by canonical correlation analysis (CCA). Moreover, we iteratively refine the predicted reference jaw shape by a multi-layer mapping and refinement (MMR) scheme. Experimental results on clinical data containing 30 sets of computed tomography (CT) models of normal and patient subjects show that the proposed method predicts more accurate patient-specific reference jaw shapes for surgical planning than the state-of-the-art method. INDEX TERMS Canonical correlation analysis, craniomaxillofacial deformity, multi-layer mapping and refinement, sparse representation.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space

Teng

Cheng

et al. 2020

IEEE Access

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An intelligent system for craniomaxillofacial defecting reconstruction

Xiong

Guo

et al. 2022

Int J of Intelligent Sys

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Craniomaxillofacial defects caused by congenital or acquired reasons seriously affect patients' physical and mental health. How to accurately and objectively repair the morphology of craniomaxillofacial tissues and organs through surgery is a difficult problem, and the preoperative virtual design is crucial. Traditional preoperative virtual design methods include mirror technology, statistical shape model, and deformable template. However, these methods are complex, time-consuming, and only applicable to some types of defects. Therefore, a general, intelligent, and personalized craniomaxillofacial defect virtual reconstruction system is desired. To solve this problem, a novel deep learning method, RecGAN, is proposed in this paper. RecGAN can learn the bone morphology of normal people, repair the defect intelligently based on the patient's remaining bone, and fully adapt to the special conditions of different patients. Currently, there are no open-source maxillofacial data sets available. Thus, a new maxillofacial computed tomography image data set with 500 simulated cases and 100 clinical cases is constructed to train and validate the method. The experimental results show that RecGAN can effectively restore the normal bone and tissue morphology of the patient's craniomaxillofacial defect area, solve the problem that there is no objective repair method for craniomaxillofacial defect, and achieve the

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Unsupervised learning of reference bony shapes for orthognathic surgical planning with a surface deformation network

et al. 2021

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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.

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Estimating patient‐specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation

Abstract: The authors have presented a novel method to automatically estimate a patient-specific reference model for the patient suffering from CMF deformity.

Cited by 20 publications

References 44 publications

Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space

Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space

An intelligent system for craniomaxillofacial defecting reconstruction

Unsupervised learning of reference bony shapes for orthognathic surgical planning with a surface deformation network

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