A new method for three-dimensional evaluation of the cranial shape and the automatic identification of craniosynostosis using 3D stereophotogrammetry

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.

3D Analysis of the Cranial and Facial Shape in Craniosynostosis Patients: A Systematic Review

With increasing interest in 3D photogrammetry, diverse methods have been developed for craniofacial shape analysis in craniosynostosis patients. This review provides an overview of these methods and offers recommendations for future studies. A systematic literature search was used to identify publications on 3D photogrammetry analyses in craniosynostosis patients until August 2023. Inclusion criteria were original research reporting on 3D photogrammetry analyses in patients with craniosynostosis and written in English. Sixty-three publications that had reproducible methods for measuring cranial, forehead, or facial shape were included in the systematic review. Cranial shape changes were commonly assessed using heat maps and curvature analyses. Publications assessing the forehead utilized volumetric measurements, angles, ratios, and mirroring techniques. Mirroring techniques were frequently used to determine facial asymmetry. Although 3D photogrammetry shows promise, methods vary widely between standardized and less conventional measurements. A standardized protocol for the selection and documentation of landmarks, planes, and measurements across the cranium, forehead, and face is essential for consistent clinical and research applications.

CranioRate: An Image-Based, Deep-Phenotyping Analysis Toolset and Online Clinician Interface for Metopic Craniosynostosis

BACKGROUND

The diagnosis and management of metopic craniosynostosis involve subjective decision-making at the point of care. The purpose of this work was to describe a quantitative severity metric and point-of-care user interface to aid clinicians in the management of metopic craniosynostosis and to provide a platform for future research through deep phenotyping.

METHODS

Two machine-learning algorithms were developed that quantify the severity of craniosynostosis-a supervised model specific to metopic craniosynostosis [Metopic Severity Score (MSS)] and an unsupervised model used for cranial morphology in general [Cranial Morphology Deviation (CMD)]. Computed tomographic (CT) images from multiple institutions were compiled to establish the spectrum of severity, and a point-of-care tool was developed and validated.

RESULTS

Over the study period (2019 to 2021), 254 patients with metopic craniosynostosis and 92 control patients who underwent CT scanning between the ages of 6 and 18 months were included. CT scans were processed using an unsupervised machine-learning based dysmorphology quantification tool, CranioRate. The average MSS was 0.0 ± 1.0 for normal controls and 4.9 ± 2.3 ( P < 0.001) for those with metopic synostosis. The average CMD was 85.2 ± 19.2 for normal controls and 189.9 ± 43.4 ( P < 0.001) for those with metopic synostosis. A point-of-care user interface (craniorate.org) has processed 46 CT images from 10 institutions.

CONCLUSIONS

The resulting quantification of severity using MSS and CMD has shown an improved capacity, relative to conventional measures, to automatically classify normal controls versus patients with metopic synostosis. The authors have mathematically described, in an objective and quantifiable manner, the distribution of phenotypes in metopic craniosynostosis.

Impact of data synthesis strategies for the classification of craniosynostosis

Introduction

Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data are rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically.

Methods

We tested the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)-based classification of craniosynostosis. The CNN is trained only on synthetic data but is validated and tested on clinical data.

Results

The combination of an SSM and a GAN achieved an accuracy of 0.960 and an F1 score of 0.928 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources.

Conclusions

Without a single clinical training sample, a CNN was able to classify head deformities with similar accuracy as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.

Cranial Morphology Associated With Syndromic Craniosynostosis: A Potential Detection of Abnormality in Patient's Cranial Growth Using Angular Statistics

INTRODUCTION

Apert, Crouzon, and Pfeiffer syndromes are common genetic syndromes related to syndromic craniosynostosis (SC), whereby it is a congenital defect that occurs when the cranial growth is distorted. Identifying cranial angles associated with these 3 syndromes may assist the surgical team to focus on a specific cranial part during the intervention planning, thus optimizing surgical outcomes and reducing potential morbidity.

OBJECTIVE

The aim of this study is to identify the cranial angles, which are associated with Apert, Crouzon, and Pfeiffer syndromes.

METHODS

The cranial computed tomography scan images of 17 patients with SC and 22 control groups aged 0 to 12 years who were treated in the University Malaya Medical Centre were obtained, while 12 angular measurements were attained using the Mimics software. The angular data were then divided into 2 groups (patients aged 0 to 24 months and >24 months). This work proposes a 95% confidence interval (CI) for angular mean to detect the abnormality in patient's cranial growth for the SC syndromes.

RESULTS

The 95% CI of angular mean for the control group was calculated and used as an indicator to confirm the abnormality in patient's cranial growth that is associated with the 3 syndromes. The results showed that there are different cranial angles associated with these 3 syndromes.

CONCLUSIONS

All cranial angles of the patients with these syndromes lie outside the 95% CI of angular mean of control group, indicating the reliability of the proposed CI in the identification of abnormality in the patient's cranial growth.

UCSQ Method Applied on 3D Photogrammetry: Non-Invasive Objective Differentiation Between Synostotic and Positional Plagiocephaly

OBJECTIVE

Objective differentiation between unilateral coronal synostosis (UCS) and positional posterior plagiocephaly (PPP) based on 3D photogrammetry according to Utrecht Cranial Shape Quantificator (UCSQ).

DESIGN

Retrospective study.

SETTING

Primary craniofacial center.

PATIENTS, PARTICIPANTS

Thirty-two unoperated patients (17 UCS; 15 PPP) (age < 1 year).

INTERVENTIONS

Extraction of variables from sinusoid curves derived using UCSQ: asymmetry ratio forehead and occiput peak, ratio of gradient forehead and occiput peak, location forehead and occiput peak.

MAIN OUTCOME MEASURE(S)

Variables, derived using 3D photogrammetry, were analyzed for differentiation between UCS and PPP.

RESULTS

Frontal peak was shifted to the right side of the head in left-sided UCS (mean x-value 207 [192-220]), and right-sided PPP (mean x-value 210 [200-216]), and to the left in right-sided UCS (mean x-value 161 [156-166]), and left-sided PPP (mean x-value 150 [144-154]). Occipital peak was significantly shifted to the right side of the head in left-sided PPP (mean x-value 338 [336-340]) and to the left in right-sided PPP (mean x-value 23 [14-32]). Mean x-value of occipital peak was 9 (354-30) in left- and 2 (350-12) in right-sided UCS. Calculated ratio of gradient of the frontal peak is, in combination with the calculated asymmetry ratio of the frontal peak, a distinctive finding.

CONCLUSIONS

UCSQ objectively captures shape of synostotic and positional plagiocephaly using 3D photogrammetry, we therefore developed a suitable method to objectively differentiate UCS from PPP using radiation-free methods.

Can Craniosynostosis be Diagnosed on Physical Examination? A Retrospective Review

Craniosynostosis is a developmental craniofacial defect in which one or more sutures of the skull fuse together prematurely. Uncorrected craniosynostosis may have serious complications including elevated intracranial pressure, developmental delay, and blindness. Proper diagnosis of craniosynostosis requires a physical examination of the head with assessment for symmetry and palpation of sutures for prominence. Often, if craniosynostosis is suspected, computed tomography (CT) imaging will be obtained. Recent literature has posited that this is unnecessary. This study aims to address whether physical examination alone is sufficient for the diagnosis and treatment planning of single suture craniosynostosis. Between 2015 and 2022, the Divisions of Pediatric Neurosurgery and Pediatric Plastic Surgery at UTHealth Houston evaluated 140 children under 36 months of age with suspected craniosynostosis by physical examination and subsequently ordered CT imaging for preoperative planning. Twenty-three patients received a clinical diagnosis of multi-sutural or syndromic craniosynostosis that was confirmed by CT. One hundred seventeen patients were diagnosed with single suture craniosynostosis on clinical examination and follow-up CT confirmed suture fusion in 109 (93.2%) patients and identified intracranial anomalies in 7 (6.0%) patients. These patients underwent surgical correction. Eight (6.8%) patients showed no evidence of craniosynostosis on CT imaging. Treatment for patients without fused sutures included molding helmets and observation alone. This evidence suggests that physical examination alone may be inadequate to accurately diagnose single suture synostosis, and surgery without preoperative CT evaluation could lead to unindicated procedures.

A New Measure of Posterior Morphology in Sagittal Craniosynostosis: The Occipital Bullet Index

INTRODUCTION

Sagittal craniosynostosis (SC) is associated with scaphocephaly, an elongated narrow head shape. Assessment of regional severity in the scaphocephalic head is limited by the use of serial computed tomographic (CT) imaging or complex computer programing. Three-dimensional measurements of cranial surface morphology provide a radiation-free alternative for assessing cranial shape. This study describes the creation of an occipital bulleting index (OBI), a novel tool using surface morphology to assess the regional severity in patients with SC.

METHODS

Surface imaging from CT scans or 3D photographs of 360 individuals with SC and 221 normocephalic individuals were compared to identify differences in morphology. Cartesian grids were created on each individual's surface mesh using equidistant axial and sagittal planes. Area under the curve (AUC) analyses were performed to identify trends in regional morphology and create measures capturing population differences.

RESULTS

The largest differences were located in the medial regions posteriorly. Using these population trends, a measure was created to maximize AUC. The OBI has an AUC of 0.72 with a sensitivity of 74% and a specificity of 61%. When the frontal bossing index is applied in tandem, the two have a sensitivity of 94.7% and a specificity of 93.1%. Correlation between the two scores in individuals with SC was found to be negligible with an intraclass correlation coefficient of 0.018. Severity was found to be independent of age under 24 months, sex, and imaging modality.

CONCLUSIONS

This index creates a tool for differentiating control head shapes from those with SC and has the potential to allow for objective evaluation of the regional severity, outcomes of different surgical techniques, and tracking shape changes in individuals over time, without the need for radiation.

Photogrammetry Applied to Neurosurgery: A Literature Review

Photogrammetry refers to the process of creating 3D models and taking measurements through the use of photographs. Photogrammetry has many applications in neurosurgery, such as creating 3D anatomical models and diagnosing and evaluating head shape and posture deformities. This review aims to summarize the uses of the technique in the neurosurgical practice and showcase the systems and software required for its implementation. A literature review was done in the online database PubMed. Papers were searched using the keywords "photogrammetry", "neurosurgery", "neuroanatomy", "craniosynostosis" and "scoliosis". The identified articles were later put through primary (abstracts and titles) and secondary (full text) screening for eligibility for inclusion. In total, 86 articles were included in the review from 315 papers identified. The review showed that the main uses of photogrammetry in the field of neurosurgery are related to the creation of 3D models of complex neuroanatomical structures and surgical approaches, accompanied by the uses for diagnosis and evaluation of patients with structural deformities of the head and trunk, such as craniosynostosis and scoliosis. Additionally, three instances of photogrammetry applied for more specific aims, namely, cervical spine surgery, skull-base surgery, and radiosurgery, were identified. Information was extracted on the software and systems used to execute the method. With the development of the photogrammetric method, it has become possible to create accurate 3D models of physical objects and analyze images with dedicated software. In the neurosurgical setting, this has translated into the creation of anatomical teaching models and surgical 3D models as well as the evaluation of head and spine deformities. Through those applications, the method has the potential to facilitate the education of residents and medical students and the diagnosis of patient pathologies.

3D Photography to Quantify the Severity of Metopic Craniosynostosis

This study aims to determine the utility of 3D photography for evaluating the severity of metopic craniosynostosis (MCS) using a validated, supervised machine learning (ML) algorithm.

This single-center retrospective cohort study included patients who were evaluated at our tertiary care center for MCS from 2016 to 2020 and underwent both head CT and 3D photography within a 2-month period.

The analysis method builds on our previously established ML algorithm for evaluating MCS severity using skull shape from CT scans. In this study, we regress the model to analyze 3D photographs and correlate the severity scores from both imaging modalities.

14 patients met inclusion criteria, 64.3% male (n = 9). The mean age in years at 3D photography and CT imaging was 0.97 and 0.94, respectively. Ten patient images were obtained preoperatively, and 4 patients did not require surgery. The severity prediction of the ML algorithm correlates closely when comparing the 3D photographs to CT bone data (Spearman correlation coefficient [SCC] r = 0.75; Pearson correlation coefficient [PCC] r = 0.82).

The results of this study show that 3D photography is a valid alternative to CT for evaluation of head shape in MCS. Its use will provide an objective, quantifiable means of assessing outcomes in a rigorous manner while decreasing radiation exposure in this patient population.

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Moderne Behandlung von Kraniosynostosen

Hintergrund

Bei 13–48 % aller Lebendgeborenen treten Auffälligkeiten der Schädelform auf. Meistens ist ein lagerungsbedingter Plagiozephalus ursächlich. Bei vorzeitigem Verschluss von Schädelnähten resultieren pathognomische Deformitäten durch das kompensatorische Wachstum der umgebenden Schädelknochen. Es muss zwischen Einzelnahtsynostosen und Kraniosynostosen im Rahmen syndromaler Erkrankungen unterschieden werden.

Ziel

Diese Arbeit soll einen Überblick über Diagnostik, konservative und chirurgische Therapie von Kraniosynostosen geben.

Methoden

Narratives Review.

Ergebnis

Bei Verdacht auf eine Kraniosynostose erfolgt zunächst die klinische Beurteilung und Einschätzung durch erfahrene Untersucher. Die pathognomische Schädelform ergibt die Arbeitsdiagnose. Bestätigt wird diese durch Verfahren wie 3D-Stereofotografie und Sonographie. In komplexen Fällen können CT oder MRT notwendig sein.

Die Indikation für eine Therapie ergibt sich aus ästhetischen Gesichtspunkten und der Vorbeugung psychosozialer Folgen. Bei syndromalen Formen besteht diese insbesondere zur Vermeidung möglicher Folgen eines erhöhten Hirndrucks.

Besteht die Indikation zur Operation muss zwischen endoskopischer und offener Technik unterschieden werden. Unterschiede bestehen hier hinsichtlich Invasivität und möglichem Korrekturausmaß. Im Anschluss an die operative Behandlung schließt sich häufig eine Helmtherapie an, um das bestmögliche Ergebnis zu erreichen. Die anschließende Follow-up-Periode erstreckt sich mindestens bis zum 12. Lebensjahr. Insgesamt sollte die Behandlung im Team mit Neurochirurgen, Mund‑, Kiefer‑, Gesichtschirurgen, Kinderärzten, Augenärzten und Humangenetikern stattfinden.

A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.

Anthropometric Landmarking for Diagnosis of Cranial Deformities: Validation of an Automatic Approach and Comparison with Intra- and Interobserver Variability

Shape analysis of infant's heads is crucial to diagnose cranial deformities and evaluate head growth. Currently available 3D imaging systems can be used to create 3D head models, promoting the clinical practice for head evaluation. However, manual analysis of 3D shapes is difficult and operator-dependent, causing inaccuracies in the analysis. This study aims to validate an automatic landmark detection method for head shape analysis. The detection results were compared with manual analysis in three levels: (1) distance error of landmarks; (2) accuracy of standard cranial measurements, namely cephalic ratio (CR), cranial vault asymmetry index (CVAI), and overall symmetry ratio (OSR); and (3) accuracy of the final diagnosis of cranial deformities. For each level, the intra- and interobserver variability was also studied by comparing manual landmark settings. High landmark detection accuracy was achieved by the method in 166 head models. A very strong agreement with manual analysis for the cranial measurements was also obtained, with intraclass correlation coefficients of 0.997, 0.961, and 0.771 for the CR, CVAI, and OSR. 91% agreement with manual analysis was achieved in the diagnosis of cranial deformities. Considering its high accuracy and reliability in different evaluation levels, the method showed to be feasible for use in clinical practice for head shape analysis.

Novel Screening and Monitoring Techniques for Deformational Plagiocephaly: A Systematic Review

This article summarizes the current state of diagnostic modalities for infant craniofacial deformities and highlights capable diagnostic tools available currently to pediatricians.

Spherical harmonics to quantify cranial asymmetry in deformational plagiocephaly

Cranial deformation and deformational plagiocephaly (DP) in particular affect an important percentage of infants. The assessment and diagnosis of the deformation are commonly carried by manual measurements that provide low interuser accuracy. Another approach is the use of three-dimensional (3D) models. Nevertheless, in most cases, deformation measurements are carried out manually on the 3D model. It is necessary to develop methodologies for the detection of DP that are automatic, accurate and take profit on the high quantity of information of the 3D models. Spherical harmonics are proposed as a new methodology to identify DP from head 3D models. The ideal fitted ellipsoid for each head is computed and the orthogonal distances between head and ellipsoid are obtained. Finally, the distances are modelled using spherical harmonics. Spherical harmonic coefficients of degree 2 and order − 2 are identified as the correct ones to represent the asymmetry characteristic of DP. The obtained coefficient is compared to other anthropometric deformation indexes, such as Asymmetry Index, Oblique Cranial Length Ratio, Posterior Asymmetry Index and Anterior Asymmetry Index. The coefficient of degree 2 and order − 2 with a maximum degree of 4 is found to provide better results than the commonly computed anthropometric indexes in the detection of DP.

The Characterisation of the Craniofacial Morphology of Infants Born With Zika Virus; Innovative Approach for Public Health Surveillance and Broad Clinical Applications

Background: This study was carried out in response to the Zika virus epidemic, which constituted a public health emergency, and to the 2019 WHO calling for strengthened surveillance for the early detection of related microcephaly. The main aim of the study was to phenotype the craniofacial morphology of microcephaly using a novel approach and new measurements, and relate the characteristics to brain abnormalities in Zika-infected infants in Brazil to improve clinical surveillance.

Methods: 3D images of the face and the cranial vault of 44 Zika-infected infants and matched healthy controls were captured using a 3D stereophotogrammetry system. The CT scans of the brain of the infected infants were analysed. Principal component analysis (PCA) was applied to characterise the craniofacial morphology. In addition to the head circumference (HC), a new measurement, head height (HH), was introduced to measure the cranial vault. The level of brain abnormality present in the CT scans was assessed; the severity of parenchymal volume loss and ventriculomegaly was quantified. Student's t-test and Spearman's Rho statistical test have been applied.

Findings: The PCA identified a significant difference (p < 0.001) between the cranial vaults and the face of the Zika infants and that of the controls. Spearman's rank-order correlation coefficients show that the head height (HH) has a strong correlation (0.87 in Zika infants; 0.82 in controls) with the morphology of the cranial vaults, which are higher than the correlation with the routinely used head circumference (HC). Also, the head height (HH) has a moderate negative correlation (−0.48) with the brain abnormalities of parenchymal volume loss.

Interpretation: It is discovered that the head height (HH) is the most sensitive and discriminatory measure of the severity of cranial deformity, which should be used for clinical surveillance of the Zika syndrome, evaluation of other craniofacial syndromes and assessment of various treatment modalities.

Three-dimensional photography for intraoperative morphometric analysis in metopic craniosynostosis surgery

PURPOSE

Surgical correction of metopic craniosynostosis typically involves open cranial vault remodeling. Accurate translation of the virtual surgical plan into the operating room is challenging due to the lack of tools for intraoperative analysis of the surgical outcome. This study aimed to evaluate the feasibility of using a hand-held 3D photography device for intraoperative evaluation and guidance during cranial vault surgical reconstruction.

METHODS

A hand-held structured light scanner was used for intraoperative 3D photography during five craniosynostosis surgeries, obtaining 3D models of skin and bone surfaces before and after the remodeling. The accuracy of this device for 3D modeling and morphology quantification was evaluated using preoperative computed tomography imaging as gold-standard. In addition, the time required for intraoperative 3D photograph acquisition was measured.

RESULTS

The average error of intraoperative 3D photography was 0.30 mm. Moreover, the interfrontal angle and the transverse forehead width were accurately measured in the 3D photographs with an average error of 0.72 degrees and 0.62 mm. Surgeon's feedback indicates that this technology can be integrated into the surgical workflow without substantially increasing surgical time.

CONCLUSION

Hand-held 3D photography is an accurate technique for objective quantification of intraoperative cranial vault morphology and guidance during metopic craniosynostosis surgical reconstruction. This noninvasive technique does not substantially increase surgical time and does not require exposure to ionizing radiation, presenting a valuable alternative to computed tomography imaging. The proposed methodology can be integrated into the surgical workflow to assist during cranial vault remodeling and ensure optimal surgical outcomes.

Three-Dimensional Stereophotogrammetry in the Evaluation of Craniosynostosis: Current and Potential Use Cases

ABSTRACT

Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes.

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to investigate if a deep learning algorithm is capable of correctly classifying the head shape of infants as either healthy controls, or as one of the following three craniosynostosis subtypes; scaphocephaly, trigonocephaly or anterior plagiocephaly. In order to acquire cranial shape data, 3D stereophotographs were made during routine pre-operative appointments of scaphocephaly (n = 76), trigonocephaly (n = 40) and anterior plagiocephaly (n = 27) patients. 3D Stereophotographs of healthy infants (n = 53) were made between the age of 3-6 months. The cranial shape data was sampled and a deep learning network was used to classify the cranial shape data as either: healthy control, scaphocephaly patient, trigonocephaly patient or anterior plagiocephaly patient. For the training and testing of the deep learning network, a stratified tenfold cross validation was used. During testing 195 out of 196 3D stereophotographs (99.5%) were correctly classified. This study shows that trained deep learning algorithms, based on 3D stereophotographs, can discriminate between craniosynostosis subtypes and healthy controls with high accuracy.

Automated Measurement of Intracranial Volume Using Three-Dimensional Photography

BACKGROUND

Current methods to analyze three-dimensional photography do not quantify intracranial volume, an important metric of development. This study presents the first noninvasive, radiation-free, accurate, and reproducible method to quantify intracranial volume from three-dimensional photography.

METHODS

In this retrospective study, cranial bones and head skin were automatically segmented from computed tomographic images of 575 subjects without cranial abnormality (average age, 5 ± 5 years; range, 0 to 16 years). The intracranial volume and the head volume were measured at the cranial vault region, and their relation was modeled by polynomial regression, also accounting for age and sex. Then, the regression model was used to estimate the intracranial volume of 30 independent pediatric patients from their head volume measured using three-dimensional photography. Evaluation was performed by comparing the estimated intracranial volume with the true intracranial volume of these patients computed from paired computed tomographic images; two growth models were used to compensate for the time gap between computed tomographic and three-dimensional photography.

RESULTS

The regression model estimated the intracranial volume of the normative population from the head volume calculated from computed tomographic images with an average error of 3.81 ± 3.15 percent (p = 0.93) and a correlation (R) of 0.96. The authors obtained an average error of 4.07 ± 3.01 percent (p = 0.57) in estimating the intracranial volume of the patients from three-dimensional photography using the regression model.

CONCLUSION

Three-dimensional photography with image analysis provides measurement of intracranial volume with clinically acceptable accuracy, thus offering a noninvasive, precise, and reproducible method to evaluate normal and abnormal brain development in young children.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Diagnostic, V.

Photogrammetry vs CT Scan: Evaluation of Accuracy of a Low-Cost Three-Dimensional Acquisition Method for Forensic Facial Approximation

Photogrammetry is a three-dimensional acquisition method potentially applicable to the forensic field. This possibility requires the verification of its accuracy. In this study, 3D volumes of skulls are generated to compare the photogrammetry versus the CT scan. In order to provide eligible material to the photogrammetric software, photographs were captured at a distance of 30 cm from the skull placed on a support 1 m in height and illuminated with diffused laboratory ceiling artificial light. A Nikon Coolpix P7100 camera was used. Photographs capture common elements with the previous and the next photograph so as to allow the photogrammetric software to recognize these common points between photographs and create a 3D puzzle. The Zephyr Lite (3DFlow©) software was employed to register the 3D volume. CT-based skulls are taken as a metric reference. The photogrammetry-based skulls are then enlarged according to the measurements of some landmarks or Zygion and Zygion, the distance between end of nasal and base of nasal pyramid for frontal projection, and minimum breadth of the mandibular ramus for the right lateral projection. The accuracy of the photogrammetry is compared to that of the CT scan by measuring the 3D volumes of the skulls studied. Specific landmarks are used as reference points for the measures in both frontal and lateral views. Bland-Altman graph shows homogeneity. The mean difference (1.28 mm) indicates that the measurements taken on the photogrammetry-based skull tend to slightly overestimate compared with the measurements taken on the CT-based skull.

Quantification of Head Shape from Three-Dimensional Photography for Presurgical and Postsurgical Evaluation of Craniosynostosis

BACKGROUND

Evaluation of surgical treatment for craniosynostosis is typically based on subjective visual assessment or simple clinical metrics of cranial shape that are prone to interobserver variability. Three-dimensional photography provides cheap and noninvasive information to assess surgical outcomes, but there are no clinical tools to analyze it. The authors aim to objectively and automatically quantify head shape from three-dimensional photography.

METHODS

The authors present an automatic method to quantify intuitive metrics of local head shape from three-dimensional photography using a normative statistical head shape model built from 201 subjects. The authors use these metrics together with a machine learning classifier to distinguish between patients with (n = 266) and without (n = 201) craniosynostosis (aged 0 to 6 years). The authors also use their algorithms to quantify objectively local surgical head shape improvements on 18 patients with presurgical and postsurgical three-dimensional photographs.

RESULTS

The authors' methods detected craniosynostosis automatically with 94.74 percent sensitivity and 96.02 percent specificity. Within the data set of patients with craniosynostosis, the authors identified correctly the fused sutures with 99.51 percent sensitivity and 99.13 percent specificity. When the authors compared quantitatively the presurgical and postsurgical head shapes of patients with craniosynostosis, they obtained a significant reduction of head shape abnormalities (p < 0.05), in agreement with the treatment approach and the clinical observations.

CONCLUSIONS

Quantitative head shape analysis and three-dimensional photography provide an accurate and objective tool to screen for head shape abnormalities at low cost and avoiding imaging with radiation and/or sedation. The authors' automatic quantitative framework allows for the evaluation of surgical outcomes and has the potential to detect relapses.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Diagnostic, I.

Statistical shape analyses of trigonocephaly patients

PURPOSE

Surgery is the first treatment option for patients with metopic craniosynostosis. Fronto-orbital advancement is the preferred method for correction of isolated trigonocephaly, but it is hard to understand whether surgery has been successful mainly in an early period. We aim to investigate the shape differences in the head shapes of trigonocephaly patients compared between preoperative and postoperative term.

METHODS

Cranial shape data were collected from the two-dimensional digital images. The Generalized Procrustes analysis was used to obtain mean shapes of the preoperative and postoperative term. The shape deformation of the frontal calvarium from preoperative to the postoperative term was evaluated using the thin-plate spline (TPS) method.

RESULTS

There was significant cranial shape difference between preoperative and postoperative term. The high-level deformations for preoperative to postoperative term determined seen in TPS graphic. Highest deformation was observed at the bifrontal dimension especially at nasion and posterior edge of the forehead.

CONCLUSIONS

In this study, we showed that the shape difference and structural deformation of the calvarium were correlated with the metopic craniosynostosis. The present study also shows that preoperative and postoperative head shapes of patients with trigonocephaly can be compared using the landmark-based geometrical morphometric method by taking into consideration the topographic distribution.

Three-dimensional facial development of children with unilateral cleft lip and palate during the first year of life in comparison with normative average faces

BACKGROUND

Stereophotogrammetry can be used to study facial morphology in both healthy individuals as well as subjects with orofacial clefts because it shows good reliability, ability to capture images rapidly, archival capabilities, and high resolution, and does not require ionizing radiation. This study aimed to compare the three-dimensional (3D) facial morphology of infants born with unilateral cleft lip and palate (UCLP) with an age-matched normative 3D average face before and after primary closure of the lip and soft palate.

METHODS

Thirty infants with a non-syndromic complete unilateral cleft lip, alveolus, and palate participated in the study. Three-dimensional images were acquired at 3, 6, 9, and 12 months of age. All subjects were treated according to the primary surgical protocol consisting of surgical closure of the lip and the soft palate at 6 months of age. Three-dimensional images of UCLP patients at 3, 6 (pre-treatment), 9, and 12 months of age were superimposed on normative datasets of average facial morphology using the children's reference frame. Distance maps of the complete 3D facial surface and the nose, upper lip, chin, forehead, and cheek regions were developed.

RESULTS

Assessments of the facial morphology of UCLP and control subjects by using color-distance maps showed large differences in the upper lip region at the location of the cleft defect and an asymmetry at the nostrils at 3 and 6 months of age. At 9 months of age, the labial symmetry was completely restored although the tip of the nose towards the unaffected side showed some remnant asymmetry. At 12 months of age, the symmetry of the nose improved, with only some remnant asymmetry noted on both sides of the nasal tip. At all ages, the mandibular and chin regions of the UCLP patients were 2.5-5 mm posterior to those in the average controls.

CONCLUSION

In patients with UCLP deviations from the normative average 3D facial morphology of age-matched control subjects existed for the upper lip, nose, and even the forehead before lip and soft palate closure was performed. Compared to the controls symmetry in the upper lip was restored, and the shape of the upper lip showed less variation after primary lip and soft palate closure. At this early age, retrusion of the soft-tissue mandible and chin, however, seems to be developing already.

Three-dimensional surface models of autopsied human brains constructed from multiple photographs by photogrammetry

Virtual three-dimensional (3D) surface models of autopsied human brain hemispheres were constructed by integrating multiple two-dimensional (2D) photographs. To avoid gravity-dependent deformity, formalin-fixed hemispheres were placed on non-refractile, transparent acrylic plates, which allowed us to take 2D photographs from various different angles. Photogrammetric calculations using software (ReCap Pro cloud service, Autodesk, San Rafael, CA, USA) allowed us calculate the 3D surface of each brain hemisphere. Virtual brain models could be moved and rotated freely to allow smooth, seamless views from different angles and different magnifications. When viewing rotating 3D models on 2D screens, 3D aspects of the models were enhanced using motion parallax. Comparison of different brains using this method allowed us to identify disease-specific patterns of macroscopic atrophy, that were not apparent in conventional 2D photographs. For example, we observed frontal lobe atrophy in a progressive supranuclear palsy brain, and even more subtle atrophy in the superior temporal gyrus in amyotrophic lateral sclerosis-frontotemporal lobar degeneration. Thus, our method facilities recognition of gyral atrophy. In addition, it provides a much more powerful and suitable way of visualizing the overall appearance of the brain as a three-dimensional structure. Comparison of normal and diseased brains will allow us to associate different macroscopic changes in the brain to clinical manifestations of various diseases.

Uniform 3D meshes to establish normative facial averages of healthy infants during the first year of life

Three-dimensional (3D) surface imaging systems are replacing direct anthropometry as the preferred method for capturing facial soft-tissues. Aims of this study were: (1) to develop normative average 3D faces of healthy infants aged 3, 6, 9, and 12 months and (2) to describe normative average 3D facial growth data in infants aged 3 to 12 months. Three-dimensional images of 50 healthy children were acquired at 3, 6, 9, and 12 months of age using the 3dMDcranial system. Four average faces with uniform meshes (3, 6, 9, and 12 months) were developed and registered based on the children’s reference frames. Distance maps of growth of the total facial surface and of the nose, upper lip, chin, forehead and cheeks for the intervals 3 to 6 months, 6 to 9 months, and 9 to 12 months of age were calculated. Mean growth of the total facial surface was 3.9 mm (standard deviation [SD] 1.2 mm), 3.5 mm (SD 0.9 mm), and 1.6 mm (SD 0.7 mm) at 3 to 6 months, 6 to 9 months, and 9 to 12 months, respectively. Regarding the selected regions of the face, the mean growth of the nose and upper lip were the largest (3.7 mm and 3.6 mm, respectively) between 6 and 9 months of age. The mean growth of the forehead, cheeks and chin were the largest (5.4 mm, 3.2, and 4.7 mm, respectively) between 3 and 6 months of age. For all facial regions, growth clearly diminished from 9 to 12 months of age. Normative data on the growth of the full face, nose, upper lip, chin, forehead and cheeks are presented. Such data can be used in future studies to identify the effectiveness of treatment of orofacial deformities such as orofacial clefts during the first year of life.

Quantitative evaluation of local head malformations from three-dimensional photography: application to craniosynostosis

The evaluation of head malformations plays an essential role in the early diagnosis, the decision to perform surgery and the assessment of the surgical outcome of patients with craniosynostosis. Clinicians rely on two metrics to evaluate the head shape: head circumference (HC) and cephalic index (CI). However, they present a high inter-observer variability and they do not take into account the location of the head abnormalities. In this study, we present an automated framework to objectively quantify the head malformations, HC, and CI from three-dimensional (3D) photography, a radiation-free, fast and non-invasive imaging modality. Our method automatically extracts the head shape using a set of landmarks identified by registering the head surface of a patient to a reference template in which the position of the landmarks is known. Then, we quantify head malformations as the local distances between the patient's head and its closest normal from a normative statistical head shape multi-atlas. We calculated cranial malformations, HC, and CI for 28 patients with craniosynostosis, and we compared them with those computed from the normative population. Malformation differences between the two populations were statistically significant (p<0.05) at the head regions with abnormal development due to suture fusion. We also trained a support vector machine classifier using the malformations calculated and we obtained an improved accuracy of 91.03% in the detection of craniosynostosis, compared to 78.21% obtained with HC or CI. This method has the potential to assist in the longitudinal evaluation of cranial malformations after surgical treatment of craniosynostosis.

Photogrammetry of the Human Brain: A Novel Method for Three-Dimensional Quantitative Exploration of the Structural Connectivity in Neurosurgery and Neurosciences

BACKGROUND

Anatomic awareness of the structural connectivity of the brain is mandatory for neurosurgeons, to select the most effective approaches for brain resections. Although standard microdissection is a validated technique to investigate the different white matter (WM) pathways and to verify the results of tractography, the possibility of interactive exploration of the specimens and reliable acquisition of quantitative information has not been described. Photogrammetry is a well-established technique allowing an accurate metrology on highly defined three-dimensional (3D) models. The aim of this work is to propose the application of the photogrammetric technique for supporting the 3D exploration and the quantitative analysis on the cerebral WM connectivity.

METHODS

The main perisylvian pathways, including the superior longitudinal fascicle and the arcuate fascicle were exposed using the Klingler technique. The photogrammetric acquisition followed each dissection step. The point clouds were registered to a reference magnetic resonance image of the specimen. All the acquisitions were coregistered into an open-source model.

RESULTS

We analyzed 5 steps, including the cortical surface, the short intergyral fibers, the indirect posterior and anterior superior longitudinal fascicle, and the arcuate fascicle. The coregistration between the magnetic resonance imaging mesh and the point clouds models was highly accurate. Multiple measures of distances between specific cortical landmarks and WM tracts were collected on the photogrammetric model.

CONCLUSIONS

Photogrammetry allows an accurate 3D reproduction of WM anatomy and the acquisition of unlimited quantitative data directly on the real specimen during the postdissection analysis. These results open many new promising neuroscientific and educational perspectives and also optimize the quality of neurosurgical treatments.

The State of Technology in Craniosynostosis

INTRODUCTION

Craniosynostosis, the premature fusion of ≥1 cranial sutures, is the leading cause of pediatric skull deformities, affecting 1 of every 2000 to 2500 live births worldwide. Technologies used for the management of craniofacial conditions, specifically in craniosynostosis, have been advancing dramatically. This article highlights the most recent technological advances in craniosynostosis surgery through a systematic review of the literature.

METHODS

A systematic electronic search was performed using the PubMed database. Search terms used were "craniosynostosis" AND "technology" OR "innovation" OR "novel.' Two independent reviewers subsequently reviewed the resultant articles based on strict inclusion and exclusion criteria. Selected manuscripts deemed novel by the senior authors were grouped by procedure categories.

RESULTS

Following review of the PubMed database, 28 of 536 articles were retained. Of the 28 articles, 20 articles consisting of 21 technologies were deemed as being novel by the senior authors. The technologies were categorized as diagnostic imaging (n = 6), surgical planning (n = 4), cranial vault evaluation (n = 4), machine learning (n = 3), ultrasound pinning (n = 3), and near-infrared spectroscopy (n = 1).

CONCLUSION

Multiple technological advances have impacted the treatment of craniosynostosis. These innovations include improvement in diagnosis and objective measurement of craniosynostosis, preoperative planning, intraoperative procedures, communication between both surgeons and patients, and surgical education.

Radiation-free quantification of head malformations in craniosynostosis patients from 3D photography

The evaluation of cranial malformations plays an essential role both in the early diagnosis and in the decision to perform surgical treatment for craniosynostosis. In clinical practice, both cranial shape and suture fusion are evaluated using CT images, which involve the use of harmful radiation on children. Three-dimensional (3D) photography offers non-invasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. The aim of this study is to develop an automated framework to objectively quantify cranial malformations in patients with craniosynostosis from 3D photography. We propose a new method that automatically extracts the cranial shape by identifying a set of landmarks from a 3D photograph. Specifically, it registers the 3D photograph of a patient to a reference template in which the position of the landmarks is known. Then, the method finds the closest cranial shape to that of the patient from a normative statistical shape multi-atlas built from 3D photographs of healthy cases, and uses it to quantify objectively cranial malformations. We calculated the cranial malformations on 17 craniosynostosis patients and we compared them with the malformations of the normative population used to build the multi-atlas. The average malformations of the craniosynostosis cases were 2.68 ± 0.75 mm, which is significantly higher (p<0.001) than the average malformations of 1.70 ± 0.41 mm obtained from the normative cases. Our approach can support the quantitative assessment of surgical procedures for cranial vault reconstruction without exposing pediatric patients to harmful radiation.

Intracranial Volume Quantification from 3D Photography

3D photography offers non-invasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. However, intracranial volume (ICV) quantification is not possible with current non-invasive imaging systems in order to evaluate brain development in children with cranial pathology. The aim of this study is to develop an automated, radiation-free framework to estimate ICV. Pairs of computed tomography (CT) images and 3D photographs were aligned using registration. We used the real ICV calculated from the CTs and the head volumes from their corresponding 3D photographs to create a regression model. Then, a template 3D photograph was selected as a reference from the data, and a set of landmarks defining the cranial vault were detected automatically on that template. Given the 3D photograph of a new patient, it was registered to the template to estimate the cranial vault area. After obtaining the head volume, the regression model was then used to estimate the ICV. Experiments showed that our volume regression model predicted ICV from head volumes with an average error of 5.81 ± 3.07% and a correlation (R²) of 0.96. We also demonstrated that our automated framework quantified ICV from 3D photography with an average error of 7.02 ± 7.76%, a correlation (R²) of 0.94, and an average estimation error for the position of the cranial base landmarks of 11.39 ± 4.3mm.