Laplacian Deformation with Symmetry Constraints for Reconstruction of Defective Skulls

Xie, Shudong; Leow, Wee Kheng; Lim, Thiam Chye

doi:10.1007/978-3-319-64698-5_3

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Recently, a method for estimating normal facial bones using geometric deformation has been proposed. 18,19 The main idea of this method is to use the estimated deformation field to deform the patient's facial defect bone to obtain its normal shape. The method can produce accurate normal bone estimation using geometric deformation calculated by the surface interpolation technique of thin-plate spline (TPS) or Laplace surface editing.…”

Section: Traditional Methodsmentioning

confidence: 99%

“…In addition, too many manual operations require a lot of processing time and a high level of professionalism. Recently, a method for estimating normal facial bones using geometric deformation has been proposed 18,19 . The main idea of this method is to use the estimated deformation field to deform the patient's facial defect bone to obtain its normal shape.…”

Section: Related Workmentioning

confidence: 99%

“…Second, it is impossible to quantitatively evaluate the postoperative effect without a comparative analysis of index data before and after an operation. Many traditional methods have been used for the reconstruction of intraoperative bone defects, such as mirror imaging mapping, [9][10][11][12][13][14][15] statistical shape model (SSM), 16,17 deformable template, 18,19 and so forth. These methods have their advantages and disadvantages, but they are only applicable to some types of defects and can not be extended to all craniomaxillofacial defects.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Traditional Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Among the interpolating methods, TPS is the most popular for reconstruction of skulls because it can tolerate noise by imposing a surface smoothness constraint through the minimization of surface bending energy. Laplacian deformation preserves local surface curvature and normal, and we are the first to apply it to skull reconstruction …”

Section: Introductionmentioning

confidence: 99%

Flip‐avoiding interpolating surface registration for skull reconstruction

Xie

Leow

Lee

et al. 2018

Robotics Computer Surgery

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Skull reconstruction is an important and challenging task in craniofacial surgery planning, forensic investigation and anthropological studies. Existing methods typically reconstruct approximating surfaces that regard corresponding points on the target skull as soft constraints, thus incurring non-zero error even for non-defective parts and high overall reconstruction error. This paper proposes a novel geometric reconstruction method that non-rigidly registers an interpolating reference surface that regards corresponding target points as hard constraints, thus achieving low reconstruction error. To overcome the shortcoming of interpolating a surface, a flip-avoiding method is used to detect and exclude conflicting hard constraints that would otherwise cause surface patches to flip and self-intersect. Comprehensive test results show that our method is more accurate and robust than existing skull reconstruction methods. By incorporating symmetry constraints, it can produce more symmetric and normal results than other methods in reconstructing defective skulls with a large number of defects. It is robust against severe outliers such as radiation artifacts in computed tomography due to dental implants. In addition, test results also show that our method outperforms thin-plate spline for model resampling, which enables the active shape model to yield more accurate reconstruction results. As the reconstruction accuracy of defective parts varies with the use of different reference models, we also study the implication of reference model selection for skull reconstruction.

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Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects

Xiao

Wang

Deng

et al. 2019

Lecture Notes in Computer Science

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In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient’s pre-traumatic photographs. An initial estimation of the patient’s normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient’s normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.

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Laplacian Deformation with Symmetry Constraints for Reconstruction of Defective Skulls

Cited by 5 publications

References 18 publications

An intelligent system for craniomaxillofacial defecting reconstruction

An intelligent system for craniomaxillofacial defecting reconstruction

Flip‐avoiding interpolating surface registration for skull reconstruction

Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects

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