Incremental Kernel Ridge Regression for the Prediction of Soft Tissue Deformations

Pan, Binbin; Xia, James J.; Yuan, Peng; Gateño, Jaime; Ip, Horace H. S.; He, Qizhen; Lee, Philip K. M.; Chow, Ben; Zhou, Xiaobo

doi:10.1007/978-3-642-33415-3_13

Cited by 14 publications

(14 citation statements)

References 7 publications

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“…[8][9][10] Among these, FEM is reported to be the most common, accurate and biomechanically relevant method. 11 Nonetheless, the prediction of facial features following orthognathic surgery is still less than ideal, especially around the nose, lips and chin regions, which are critically important for facial esthetics and in evaluating surgical outcome. Reported absolute errors in the predicted lip were greater than 2 mm.…”

Section: Introductionmentioning

confidence: 99%

A clinically validated prediction method for facial soft‐tissue changes following double‐jaw surgery

Kim

Mai

et al. 2017

Medical Physics

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Purpose It is clinically important to accurately predict facial soft tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction. Methods In Stage One, soft-tissue-change was simulated using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect using a nodal force constraint method. Results The results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any sub-regions thereof were below 1.5 mm. The overall soft tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods. Conclusion The FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach.

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Section: Introductionmentioning

confidence: 99%

A clinically validated prediction method for facial soft‐tissue changes following double‐jaw surgery

Kim

Mai

et al. 2017

Medical Physics

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“…This paper completes our conference paper [18] by including more details of our methods and additional experiments.…”

Section: Introductionmentioning

confidence: 76%

“…The idea was to replace the inner product in H with the kernel function, which could be calculated much more efficiently. Given x and y in the input space and kernel function k , we have the relation [18]…”

Section: Methodsmentioning

confidence: 99%

Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression

Pan

Zhang

Xia

et al. 2016

Computers in Biology and Medicine

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Facial soft tissue deformation following osteotomy is associated with the corresponding biomechanical characteristics of bone and soft tissues. However, none of the methods devised to predict soft tissue deformation after osteotomy incorporates population-based statistical data. The aim of this study is to establish a statistical model to describe the relationship between biomechanical characteristics and soft tissue deformation after osteotomy. We proposed an incremental kernel ridge regression (IKRR) model to accomplish this goal. The input of the model is the biomechanical information computed by the Finite Element Method (FEM). The output is the soft tissue deformation generated from the paired pre-operative and post-operative 3D images. The model is adjusted incrementally with each new patient’s biomechanical information. Therefore, the IKRR model enables us to predict potential soft tissue deformations for new patient by using both biomechanical and statistical information. The integration of these two types of data is critically important for accurate simulations of soft-tissue changes after surgery. The proposed method was evaluated by leave-one-out cross-validation using data from 11 patients. The average prediction error of our model (0.9103 mm) was lower than some state-of-the-art algorithms. This model is promising as a reliable way to prevent the risk of facial distortion after craniomaxillofacial surgery.

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“…The resolution of the cross-sectional images for gross anatomy were 3072×2048, 0.17 mm of pixel size, and 0.25 mm of image intervals. Eleven muscles (buccinator, depressor anguli oris, depressor labii, levator anguli oris, levator labii, levator labii alaeque nasi, mentalis, orbicularis oris, zygomaticus major, zygomaticus minor and masseter) 24 were segmented from the color images by 2 CMF surgeons (Z.T. and J.J.X.)…”

Section: Methodsmentioning

confidence: 99%

An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation

et al. 2015

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Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft-tissue changes following osteotomy. This can only be accomplished on an anatomically-detailed facial soft tissue model. However, current anatomically-detailed facial soft tissue model generation is not appropriate for clinical applications due to the time intensive nature of manual segmentation and volumetric mesh generation. This paper presents a novel semi-automatic approach, named eFace-template method, for efficiently and accurately generating a patient-specific facial soft tissue model. Our novel approach is based on the volumetric deformation of an anatomically-detailed template to be fitted to the shape of each individual patient. The adaptation of the template is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a Thin-Plate Spline interpolation. This methodology was validated using 4 visible human datasets (regarded as gold standards) and 30 patient models. The results indicated that our approach can accurately preserve the internal anatomical correspondence (i.e., muscles) for finite element modeling (FEM). Additionally, our hybrid approach was able to achieve an optimal balance among the patient shape fitting accuracy, anatomical correspondence and mesh quality. Furthermore, the statistical analysis showed that our hybrid approach was superior to two previously published methods: mesh-matching and landmark-based transformation. Ultimately, our eFace-template method can be directly and effectively used clinically to simulate the facial soft tissue changes in the clinical application.

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Incremental Kernel Ridge Regression for the Prediction of Soft Tissue Deformations

Cited by 14 publications

References 7 publications

A clinically validated prediction method for facial soft‐tissue changes following double‐jaw surgery

A clinically validated prediction method for facial soft‐tissue changes following double‐jaw surgery

Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression

An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation

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