Planning of mandibular reconstructions based on statistical shape models

Development of a universal cutting guide for raising deep circumflex iliac artery flaps

PURPOSE

The deep circumflex iliac crest flap (DCIA) is used for the reconstruction of the jaw. For fitting of the transplant by computer-aided planning (CAD), a computerized tomography (CT) of the jaw and the pelvis is necessary. Ready-made cutting guides save a pelvic CT and healthcare resources while maintaining the advantages of the CAD planning.

METHODS

A total of 2000 CTs of the pelvis were divided into groups of 500 by sex and age (≤ 45 and > 45 years). Three-dimensional (3D) pelvis models were aligned and averaged. Cutting guides were designed on the averaged pelvis for each group and an overall averaged pelvis. The cutting guides and 50 randomly selected iliac crests (10 from each group and 10 from the whole collective) were 3D printed. The appropriate cutting guide was mounted to the iliac crest and a cone beam CT was performed. The thickness of the space between the iliac crest and the cutting guide was evaluated.

RESULTS

Overall the mean thickness of the space was 2.137 mm and the mean volume of the space was 4513 mm3. The measured values were significantly different between the different groups. The overall averaged group had not the greatest volume, maximum thickness and mean thickness of the space.

CONCLUSION

Ready-made cutting guides for the DCIA flap fit to the iliac crest and make quick and accurate flap raising possible while radiation dose and resources can be saved. The cutting guides fit sufficient to the iliac crest and should keep the advantages of a standard CAD planning.

A statistical shape model for estimating missing soft tissues of the face in a black South African population

PURPOSE

Facial disfigurement may affect the quality of life of many patients. Facial prostheses are often used as an adjuvant to surgical intervention and may sometimes be the only viable treatment option. Traditional methods for designing soft-tissue facial prostheses are time-consuming and subjective, while existing digital techniques are based on mirroring of contralateral features of the patient, or the use of existing feature templates/models that may not be readily available. We aim to support the objective and semi-automated design of facial prostheses with primary application to midline or bilateral defect restoration where no contralateral features are present. Specifically, we developed and validated a statistical shape model (SSM) for estimating the shape of missing facial soft tissue segments, from any intact parts of the face.

MATERIALS AND METHODS

An SSM of 3D facial variations was built from meshes extracted from computed tomography and cone beam computed tomography images of a black South African sample (n = 235) without facial disfigurement. Various types of facial defects were simulated, and the missing parts were estimated automatically by a weighted fit of each mesh to the SSM. The estimated regions were compared to the original regions using color maps and root-mean-square (RMS) distances.

RESULTS

Root mean square errors (RMSE) for defect estimations of one orbit, partial nose, cheek, and lip were all below 1.71 mm. Errors for the full nose, bi-orbital defects, as well as small and large composite defects were between 2.10 and 2.58 mm. Statistically significant associations of age and type of defect with RMSE were observed, but not with sex or imaging modality.

CONCLUSION

This method can support the objective and semi-automated design of facial prostheses, specifically for defects in the midline, crossing the midline or bilateral defects, by facilitating time-consuming and skill-dependent aspects of prosthesis design.

An open-source, three-dimensional growth model of the mandible

The available reference data for the mandible and mandibular growth consists primarily of two-dimensional linear or angular measurements. The aim of this study was to create the first open-source, three-dimensional statistical shape model of the mandible that spans the complete growth period. Computed tomography scans of 678 mandibles from children and young adults between 0 and 22 years old were included in the model. The mandibles were segmented using a semi-automatic or automatic (artificial intelligence-based) segmentation method. Point correspondence among the samples was achieved by rigid registration, followed by non-rigid registration of a symmetrical template onto each sample. The registration process was validated with adequate results. Principal component analysis was used to gain insight in the variation within the dataset and to investigate age-related changes and sexual dimorphism. The presented growth model is accessible globally and free-of-charge for scientists, physicians and forensic investigators for any kind of purpose deemed suitable. The versatility of the model opens up new possibilities in the fields of oral and maxillofacial surgery, forensic sciences or biological anthropology. In clinical settings, the model may aid diagnostic decision-making, treatment planning and treatment evaluation.

Shape variation and sex differences of the adult human mandible evaluated by geometric morphometrics

In cases of osseous defects, knowledge of the anatomy, and its age and sex-related variations, is essential for reconstruction of normal morphology. Here, we aimed at creating a 3D atlas of the human mandible in an adult sample using dense landmarking and geometric morphometrics. We segmented 50 male and 50 female mandibular surfaces from CBCT images (age range: 18.9-73.7 years). Nine fixed landmarks and 510 sliding semilandmarks were digitized on the mandibular surface, and then slid by minimizing bending energy against the average shape. Principal component analysis extracted the main patterns of shape variation. Sexes were compared with permutation tests and allometry was assessed by regressing on the log of the centroid size. Almost 49 percent of shape variation was described by the first three principal components. Shape variation was related to width, height and length proportions, variation of the angle between ramus and corpus, height of the coronoid process and inclination of the symphysis. Significant sex differences were detected, both in size and shape. Males were larger than females, had a higher ramus, more pronounced gonial angle, larger inter-gonial width, and more distinct antegonial notch. Accuracy of sexing based on the first two principal components in form space was 91 percent. The degree of edentulism was weakly related to mandibular shape. Age effects were not significant. The resulting atlas provides a dense description of mandibular form that can be used clinically as a guide for planning surgical reconstruction.

Development and validation of a novel craniofacial statistical shape model for the virtual reconstruction of bilateral maxillary defects

Bilateral maxillary defects are a challenge for fibula free flap reconstruction (FFFR) surgery due to limitations in virtual surgical planning (VSP) workflows. While meshes of unilateral defects can be mirrored to virtually reconstruct missing anatomy, Brown class c and d defects lack a contralateral reference and associated anatomical landmarks. This often results in poor placement of osteotomized fibula segments. This study was performed to improve the VSP workflow for FFFR using statistical shape modeling (SSM) - a form of unsupervised machine learning - to virtually reconstruct premorbid anatomy in an automated, reproducible, and patient-specific manner. A training set of 112 computed tomography scans was sourced from an imaging database by stratified random sampling. The craniofacial skeletons were segmented, aligned, and processed via principal component analysis. Reconstruction performance was validated on a set of 45 unseen skulls containing various digitally generated defects (Brown class IIa-d). Validation metrics demonstrated promising accuracy: mean 95th percentile Hausdorff distance of 5.47 ± 2.39 mm, mean volumetric Dice coefficient of 48.8 ± 14.5%, compactness of 7.28 × 105 mm2, specificity of 1.18 mm, and generality of 8.12 × 10-6 mm. SSM-guided VSP will allow surgeons to create patient-centric treatment plans, increasing FFFR accuracy, reducing complications, and improving postoperative outcomes.

From bench to bedside - current clinical and translational challenges in fibula free flap reconstruction

Fibula free flaps (FFF) represent a working horse for different reconstructive scenarios in facial surgery. While FFF were initially established for mandible reconstruction, advancements in planning for microsurgical techniques have paved the way toward a broader spectrum of indications, including maxillary defects. Essential factors to improve patient outcomes following FFF include minimal donor site morbidity, adequate bone length, and dual blood supply. Yet, persisting clinical and translational challenges hamper the effectiveness of FFF. In the preoperative phase, virtual surgical planning and artificial intelligence tools carry untapped potential, while the intraoperative role of individualized surgical templates and bioprinted prostheses remains to be summarized. Further, the integration of novel flap monitoring technologies into postoperative patient management has been subject to translational and clinical research efforts. Overall, there is a paucity of studies condensing the body of knowledge on emerging technologies and techniques in FFF surgery. Herein, we aim to review current challenges and solution possibilities in FFF. This line of research may serve as a pocket guide on cutting-edge developments and facilitate future targeted research in FFF.

Construction of Shape Atlas for Abdominal Organs using Three-Dimensional Mesh Variational Autoencoder

A model that represents the shapes and positions of organs or skeletal structures with a small number of parameters may be expected to have a wide range of clinical applications, such as radiotherapy and surgical guidance. However, because soft organs vary in shape and position between patients, it is difficult for linear models to reconstruct locally variable shapes, and nonlinear models are prone to overfitting, particularly when the quantity of data is small. The aim of this study was to construct a shape atlas with high accuracy and good generalization performance. We designed a mesh variational autoencoder that can reconstruct both nonlinear shape and position with high accuracy. We validated the trained model for liver meshes of 125 cases, and found that it was possible to reconstruct the positions and shapes with an average accuracy of 4.3 mm for the test data of 19 cases.

Morphological Variation of the Mandible in the Orthognathic Population-A Morphological Study Using Statistical Shape Modelling

The aim of this study was to investigate the value of 3D Statistical Shape Modelling for orthognathic surgery planning. The goal was to objectify shape variations in the orthognathic population and differences between male and female patients by means of a statistical shape modelling method. Pre-operative CBCT scans of patients for whom 3D Virtual Surgical Plans (3D VSP) were developed at the University Medical Center Groningen between 2019 and 2020 were included. Automatic segmentation algorithms were used to create 3D models of the mandibles, and the statistical shape model was built through principal component analysis. Unpaired t-tests were performed to compare the principal components of the male and female models. A total of 194 patients (130 females and 64 males) were included. The mandibular shape could be visually described by the first five principal components: (1) The height of the mandibular ramus and condyles, (2) the variation in the gonial angle of the mandible, (3) the width of the ramus and the anterior/posterior projection of the chin, (4) the lateral projection of the mandible's angle, and (5) the lateral slope of the ramus and the inter-condylar distance. The statistical test showed significant differences between male and female mandibular shapes in 10 principal components. This study demonstrates the feasibility of using statistical shape modelling to inform physicians about mandible shape variations and relevant differences between male and female mandibles. The information obtained from this study could be used to quantify masculine and feminine mandibular shape aspects and to improve surgical planning for mandibular shape manipulations.

Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network

PURPOSE

For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that reduce the quality of the computer-tomographic imaging data. This study provides a method to segment accurate, artifact-free 3D surface models of mandibles from CT data using convolutional neural networks.

METHODS

The presented approach cascades two independently trained 3D-U-Nets to perform accurate segmentations of the mandible bone from full resolution CT images. The networks are trained in different settings using three different loss functions and a data augmentation pipeline. Training and evaluation datasets consist of manually segmented CT images from 307 dentate and edentulous individuals, partly with heavy imaging artifacts. The accuracy of the models is measured using overlap-based, surface-based and anatomical-curvature-based metrics.

RESULTS

Our approach produces high-resolution segmentations of the mandibles, coping with severe imaging artifacts in the CT imaging data. The use of the two-stepped approach yields highly significant improvements to the prediction accuracies. The best models achieve a Dice coefficient of 94.824% and an average surface distance of 0.31 mm on our test dataset.

CONCLUSION

The use of two cascaded U-Net allows high-resolution predictions for small regions of interest in the imaging data. The proposed method is fast and allows a user-independent image segmentation, producing objective and repeatable results that can be used in automated surgical planning procedures.

Reconstruction of the mandible from partial inputs for virtual surgery planning

Statistical Shape Models (SSMs) and Sparse Prediction Models (SPMs) based on regressions between cephalometric measurements were compared against standard practice in virtual surgery planning for reconstruction of mandibular defects. Emphasis was placed on the ability of the models to reproduce clinically relevant metrics. CT scans of 50 men and 50 women were collected and split into training and testing datasets according to an 80:20 ratio. The scans were segmented, and anatomical landmarks were identified. SPMs were constructed based on direct regressions between measurements derived from the anatomical landmarks. SSMs were developed by establishing correspondence between the segmented meshes, performing alignment, and principal component analysis. Anterior and bilateral defects were simulated by removing sections of the mandibles in the testing set. Measurement errors after reconstruction ranged from 1.07˚ to 2.2˚ and 0.66 mm to 2.02 mm for mirroring, from 0.45˚ to 3.67˚ and 0.66 mm to 2.54 mm for the SSMs, and from 1.74˚ to 5.01˚ and 0.64 mm to 2.89 mm for the SPMs. Surface-to-surface errors ranged from 1.01 mm to 1.29 mm and 1.06 mm to 1.33 mm for mirroring and SSMs, respectively. Based on the results, SSMs are recommended for VSP in the absence of normal patient anatomy.

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

PURPOSE

Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans.

METHODS

The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD).

RESULTS

Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] - 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] - 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] - 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] - 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen.

CONCLUSION

In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.

[Digitalization and use of artificial intelligence in microvascular reconstructive facial surgery]

BACKGROUND

When using digitalization and artificial intelligence (AI), large amounts of data (big data) are produced, which can be processed by computers and used in the field of microvascular-reconstructive craniomaxillofacial surgery (CMFS).

OBJECTIVE

The aim of this article is to summarize current applications of digitalized medicine and AI in microvascular reconstructive CMFS.

MATERIAL AND METHODS

Review of frequent applications of digital medicine for microvascular CMFS reconstruction, focusing on digital planning, navigation, robotics and potential applications with AI.

RESULTS

The broadest utilization of medical digitalization is in the virtual planning of microvascular transplants, individualized implants and template-guided reconstruction. Navigation is commonly used for ablative tumor surgery but less frequently in reconstructions. Robotics are mainly employed in the transoral approach for tumor surgery of the hypopharynx, whereas the use of AI is still limited even if possible applications would be automated virtual planning and monitoring systems.

CONCLUSION

The use of digitalized methods and AI are adjuncts to microvascular reconstruction. Automatization approaches and simplification of technologies will provide such applications to a broader clientele in the future; however, in CMFS, robotic-assisted resections and automated flap monitoring are not yet the standard of care.

Predicting the Premorbid Shape of a Diseased Mandible

OBJECTIVES/HYPOTHESIS

Virtual surgical planning (VSP) for reconstructions of advanced mandibular neoplasms that have distorted the contour of the mandible is challenging, as the premorbid shape of the mandible is unknown. We introduce a novel modeling technique, based on a statistical shape model (SSM), that has learned the shape of a normal mandible from a set of 84 mandibles, such that given a diseased mandible, the model can determine its premorbid shape.

METHODS

Eighty-four control mandibles were used to generate an SSM. Various mandibular defects were created, and the SSM was applied to predict the shape of the original mandible. The predicted and original shape of the defect were compared for accuracy using volumetric overlap and Hausdorff distance. All mandibular VSP cases in the past 2 years were reviewed to identify those that required virtual preprocessing due to significantly distorted mandibular contours. The SSM was compared to those cases requiring preprocessing and highlighted in one prospective VSP.

RESULTS

The average volumetric overlap and Hausdorff distance between the defect replacement and the defect are 73.9% ± 13.3% and 4.51 mm ± 2.65 mm, respectively. The SSM is more accurate for smaller defects, and those not including the condyle. Ten out of 40 VSP cases required preprocessing using four different techniques. Qualitatively, the SSM outperformed those preprocessing techniques applied in the retrospective cases.

CONCLUSIONS

The SSM can accurately predict the premorbid shape of a distorted mandible and is superior to current preprocessing techniques. The SSM was successfully applied to a retrospective series and one prospective index case.

LEVEL OF EVIDENCE

4 Laryngoscope, 131:E781-E786, 2021.

A review on multiplatform evaluations of semi-automatic open-source based image segmentation for cranio-maxillofacial surgery

BACKGROUND AND OBJECTIVES

Computer-assisted technologies, such as image-based segmentation, play an important role in the diagnosis and treatment support in cranio-maxillofacial surgery. However, although many segmentation software packages exist, their clinical in-house use is often challenging due to constrained technical, human or financial resources. Especially technological solutions or systematic evaluations of open-source based segmentation approaches are lacking. The aim of this contribution is to assess and review the segmentation quality and the potential clinical use of multiple commonly available and license-free segmentation methods on different medical platforms.

METHODS

In this contribution, the quality and accuracy of open-source segmentation methods was assessed on different platforms using patient-specific clinical CT-data and reviewed with the literature. The image-based segmentation algorithms GrowCut, Robust Statistics Segmenter, Region Growing 3D, Otsu & Picking, Canny Segmentation and Geodesic Segmenter were investigated in the mandible on the platforms 3D Slicer, MITK and MeVisLab. Comparisons were made between the segmentation algorithms and the ground truth segmentations of the same anatomy performed by two clinical experts (n = 20). Assessment parameters were the Dice Score Coefficient (DSC), the Hausdorff Distance (HD), and Pearsons correlation coefficient (r).

RESULTS

The segmentation accuracy was highest with the GrowCut (DSC 85.6%, HD 33.5 voxel) and the Canny (DSC 82.1%, HD 8.5 voxel) algorithm. Statistical differences between the assessment parameters were not significant (p < 0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the segmentation methods and the ground truth schemes. Functionally stable and time-saving segmentations were observed.

CONCLUSION

High quality image-based semi-automatic segmentation was provided by the GrowCut and the Canny segmentation method. In the cranio-maxillofacial complex, these segmentation methods provide algorithmic alternatives for image-based segmentation in the clinical practice for e.g. surgical planning or visualization of treatment results and offer advantages through their open-source availability. This is the first systematic multi-platform comparison that evaluates multiple license-free, open-source segmentation methods based on clinical data for the improvement of algorithms and a potential clinical use in patient-individualized medicine. The results presented are reproducible by others and can be used for clinical and research purposes.

The morphology of the human mandible: A computational modelling study

Cephalometric methods have been used to evaluate morphometric measurements of the mandible and quantify sex-related anatomical features; however, most studies to date employ a limited set of location-specific measurements without considering the entire three-dimensional anatomy of the mandible. The aims of this study were to develop statistical shape models (SSMs) of partially edentulous male and female mandibles to evaluate inter-subject morphological variability and secondly to assess the effectiveness of discrete clinical morphometric measurements in prediction of complete three-dimensional mandible geometry. Computed tomography images of forty partially edentulous female and twenty-five male subjects were obtained, and SSM developed using mesh fitting, rigid body registration and principal component analysis. Analysis of female and male SSMs showed that the variation along their first principal components was size-related. Sex-differentiating pure shape variations were found along the first principal component of size-normalised SSM and were observed to be most prominent in the symphysis and posterior ramus regions of the mandible. Seven morphometric measurements were found to characterise female and male shape prediction optimally. The capability to rapidly generate accurate patient-specific shape-predictive models of the mandible may be useful for implant development and pre-operative planning, particularly in the absence of bony structures following trauma or tumour resection.

Morphological analysis of sigmoid sinus anatomy: clinical applications to neurotological surgery

OBJECTIVES

The primary objective of this study was to use high-resolution micro-CT images to create accurate three-dimensional (3D) models of several intratemporal structures, and to compare several surgically important dimensions within the temporal bone. The secondary objective was to create a statistical shape model (SSM) of a dominant and non-dominant sigmoid sinus (SS) to provide a template for automated segmentation algorithms.

METHODS

A free image processing software, 3D Slicer, was utilized to create three-dimensional reconstructions of the SS, jugular bulb (JB), facial nerve (FN), and external auditory canal (EAC) from micro-CT scans. The models were used to compare several clinically important dimensions between the dominant and non-dominant SS. Anatomic variability of the SS was also analyzed using SSMs generated using the Statismo software framework.

RESULTS

Three-dimensional models from 38 temporal bones were generated and analyzed. Right dominance was observed in 74% of the paired SSs. All distances were significantly shorter on the dominant side (p < 0.05), including: EAC - SS (dominant: 13.7 ± 3.4 mm; non-dominant: 15.3 ± 2.7 mm), FN - SS (dominant: 7.2 ± 1.8 mm; non-dominant: 8.1 ± 2.3 mm), 2nd genu FN - superior tip of JB (dominant: 8.7 ± 2.2 mm; non-dominant: 11.2 ± 2.6 mm), horizontal distance between the superior tip of JB - descending FN (dominant: 9.5 ± 2.3 mm; non-dominant: 13.2 ± 3.5 mm), and horizontal distance between the FN at the stylomastoid foramen - JB (dominant: 5.4 ± 2.2 mm; non-dominant: 7.7 ± 2.1). Analysis of the SSMs indicated that SS morphology is most variable at its junction with the transverse sinus, and least variable at the JB.

CONCLUSIONS

This is the first known study to investigate the anatomical variation and relationships of the SS using high resolution scans, 3D  models and statistical shape analysis. This analysis seeks to guide neurotological surgical approaches and provide a template for automated segmentation and surgical simulation.

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action

INTRODUCTION

Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice.

MATERIAL AND METHODS

In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance.

RESULTS

Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups.

DISCUSSION

Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

Introduction of an algorithm for planning of autologous fibular transfer in mandibular reconstruction based on individual bone curvatures

BACKGROUND

Reconstruction of the mandible with autologous transplants is a challenging task and current computer-aided surgical planning remains cumbersome. Thus, the aim of the present study was to create an automated computational approach for this procedure.

METHODS

The developed algorithm is based on curves following characteristic anatomical features. Geometrical data from a physiological mandible and a fibula were used to generate six different defects, and geometrical accordance was investigated to demonstrate the applicability of the method with different reconstruction parameters (n = 309).

RESULTS

The method proved to be applicable, it recognized given clinical constraints and the values of accordance could be used to quantify the success of reconstructions.

CONCLUSIONS

With the present approach, the complex three-dimensional task of mandibular reconstruction was simplified, and thus it allows implementation in clinical routine. The computational planning that is proposed may be used to design cutting guides or as geometrical input data for real-time navigated surgery.

Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty

PURPOSE

Spring-assisted cranioplasty is performed to correct the long and narrow head shape of children with sagittal synostosis. Such corrective surgery involves osteotomies and the placement of spring-like distractors, which gradually expand to widen the skull until removal about 4 months later. Due to its dynamic nature, associations between surgical parameters and post-operative 3D head shape features are difficult to comprehend. The current study aimed at applying population-based statistical shape modelling to gain insight into how the choice of surgical parameters such as craniotomy size and spring positioning affects post-surgical head shape.

METHODS

Twenty consecutive patients with sagittal synostosis who underwent spring-assisted cranioplasty at Great Ormond Street Hospital for Children (London, UK) were prospectively recruited. Using a nonparametric statistical modelling technique based on mathematical currents, a 3D head shape template was computed from surface head scans of sagittal patients after spring removal. Partial least squares (PLS) regression was employed to quantify and visualise trends of localised head shape changes associated with the surgical parameters recorded during spring insertion: anterior-posterior and lateral craniotomy dimensions, anterior spring position and distance between anterior and posterior springs.

RESULTS

Bivariate correlations between surgical parameters and corresponding PLS shape vectors demonstrated that anterior-posterior (Pearson's [Formula: see text]) and lateral craniotomy dimensions (Spearman's [Formula: see text]), as well as the position of the anterior spring ([Formula: see text]) and the distance between both springs ([Formula: see text]) on average had significant effects on head shapes at the time of spring removal. Such effects were visualised on 3D models.

CONCLUSIONS

Population-based analysis of 3D post-operative medical images via computational statistical modelling tools allowed for detection of novel associations between surgical parameters and head shape features achieved following spring-assisted cranioplasty. The techniques described here could be extended to other cranio-maxillofacial procedures in order to assess post-operative outcomes and ultimately facilitate surgical decision making.