Multi-Height Extraction of Clinical Parameters Improves Classification of Craniosynostosis

Introduction: 3D surface scan-based diagnosis of craniosynostosis is a promising radiation-free alternative to traditional diagnosis using computed tomography. The cranial index (CI) and the cranial vault asymmetry index (CVAI) are well-established clinical parameters that are widely used. However, they also have the benefit of being easily adaptable for automatic diagnosis without the need of extensive preprocessing. Methods: We propose a multi-height-based classification approach that uses CI and CVAI in different height layers and compare it to the initial approach using only one layer. We use ten-fold cross-validation and test seven different classifiers. The dataset of 504 patients consists of three types of craniosynostosis and a control group consisting of healthy and non-synostotic subjects. Results: The multi-height-based approach improved classification for all classifiers. The k-nearest neighbors classifier scored best with a mean accuracy of 89 % and a mean F1-score of 0.75. Conclusion: Taking height into account is beneficial for the classification. Based on accepted and widely used clinical parameters, this might be a step towards an easy-to-understand and transparent classification approach for both physicians and patients

Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients Combining posterior shape models with a Laplace-Beltrami based approach for shape reconstruction

This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-basedclassification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, mostSSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace-Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes

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

Generative-Adversarial-Network-Based Data Augmentation for the Classification of Craniosynostosis

Craniosynostosis is a congenital disease characterized by the premature closure of one or multiple sutures of the infant’s skull. For diagnosis, 3D photogrammetric scans are a radiation-free alternative to computed tomography. However, data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet been analyzed. In this work, we use a 2D distance map representation of the infants’ heads with a convolutional-neural-network-based classifier and employ a generative adversarial network (GAN) for data augmentation. We simulate two data scarcity scenarios with 15% and 10% training data and test the influence of different degrees of added synthetic data and balancing underrepresented classes. We used total accuracy and F1-score as a metric to evaluate the final classifiers. For 15% training data, the GAN-augmented dataset showed an increased F1-score up to 0.1 and classification accuracy up to 3 %. For 10% training data, both metrics decreased. We present a deep convolutional GAN capable of creating synthetic data for the classification of craniosynostosis. Using a moderate amount of synthetic data using a GAN showed slightly better performance, but had little effect overall. The simulated scarcity scenario of 10% training data may have limited the model’s ability to learn the underlying data distribution

Multi-Height Extraction of Clinical Parameters Improves Classification of Craniosynostosis

Introduction: 3D surface scan-based diagnosis of craniosynostosis is a promising radiation-free alternative to traditional diagnosis using computed tomography. The cranial index (CI) and the cranial vault asymmetry index (CVAI) are well-established clinical parameters that are widely used. However, they also have the benefit of being easily adaptable for automatic diagnosis without the need of extensive preprocessing. Methods: We propose a multi-height-based classification approach that uses CI and CVAI in different height layers and compare it to the initial approach using only one layer. We use ten-fold cross-validation and test seven different classifiers. The dataset of 504 patients consists of three types of craniosynostosis and a control group consisting of healthy and non-synostotic subjects. Results: The multi-height-based approach improved classification for all classifiers. The k-nearest neighbors classifier scored best with a mean accuracy of 89% and a mean F1-score of 0.75. Conclusion: Taking height into account is beneficial for the classification. Based on accepted and widely used clinical parameters, this might be a step towards an easy-to-understand and transparent classification approach for both physicians and patients

A novel approach to determining augmented bone volume in intraoral bone block augmentation using an intraoral scanner: an in vitro study

Abstract Introduction Bone augmentation procedures are established tools for reshaping the alveolar ridge and increasing bone volume. Different approaches are being used to measure postoperative bone volume gain. This study aimed to develop an objective and automated volume measurement tool equally as precise as manual slice-by-slice annotation. Materials and methods To evaluate the proposed workflow, we performed an in vitro study with 20 pig mandibles that were grafted using three different grafting techniques—autogenous full block, split block bone and shell augmentation. The pig jaws were scanned pre- and postoperatively using an intraoral scanner. The resulting surface files (baseline, full block, split block, shell) were processed using the new volume-measuring workflow as well as using manual slice-by-slice annotation at baseline (t0) and at 6 months (t1) using the same population. Two TOSTs (Test of One-Sided Significance) and NHSTs (Null Hypothesis Significance Test) were used to compare the two workflows. The intra-rater reliability between t0 and t1 was determined using intraclass correlation coefficients. Results The mean difference for the full block augmentation technique was − 0.015 cm3 (p < 0.001); for the split block technique, it was − 0.034 cm3 p = 0.01, and for the shell technique, it was − 0.042 cm3. All results were statistically not different from zero and statistically equivalent to zero. The results also showed an excellent absolute intra-rater agreement. Conclusions The semiautomatic volume measurement established in this article achieves comparable results to manual slice-by-slice measuring in determining volumes on STL files generated by intraoral scanners and shows an excellent intra-rater reliability. Graphical Abstrac

Image-guided percutaneous sclerotherapy of venous malformations of the head and neck: Clinical and MR-based volumetric mid-term outcome.

ObjectiveTo report the clinical and MRI-based volumetric mid-term outcome after image guided percutaneous sclerotherapy (PS) of venous malformations (VM) of the head and neck.MethodsA retrospective analysis of a prospectively maintained database was performed, including patients with VM of the head and neck who were treated with PS. Only patients with available pre- and post-interventional MRI were included into this study. Clinical outcome, which was subjectively assessed by the patients, their parents (for paediatric patients) and/or the physicians, was categorized as worse, unchanged, minor or major improvement. Radiological outcome, determined by MRI-based volumetric measurements, was categorized as worse (>10% increase), unchanged (≤10% increase to ResultsTwenty-seven patients were treated in 51 treatment sessions. After a mean follow-up of 31 months, clinical outcome was worse for 7.4%, unchanged for 3.7% of the patients, while there was minor and major improvement for 7.4% and 81.5%, respectively. In the volumetric imaging analysis 7.4% of the VMs were worse and 14.8% were unchanged. Minor improvement was observed in 22.2%, intermediate improvement in 44.4% and major improvement in 11.1%. The rate of permanent complications was 3.7%.ConclusionPS can be an effective therapy to treat the symptoms of patients with VMs of the head and neck and to downsize the VMs. MRI-based volumetry can be used to objectively follow the change in size of the VMs after PS. Relief of symptoms frequently does not require substantial volume reduction

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

A statistical shape model of craniosynostosis patients and 100 model instances of each pathology

&lt;p&gt;This dataset is part of the publication "A statistical shape model for radiation-free assessment and classification of craniosynostosis" (M. Schaufelberger et al.). &nbsp;It includes several 3D head models constructed of surface scans of craniosynostosis patients: The full shape model, a texture model, and submodels of four classes: sagittal suture fusion (scaphocephaly), metopic suture fusion (trigonocephaly), coronal suture fusion (brachycephaly and anterior plagiocephaly), and a control model (normocephaly and positional plagiocephaly). &nbsp;Each of the models is available in an .h5 file. &nbsp;We also include 100 mesh instances as a .ply file in a zip file. &nbsp;The model's statistical information can be incorporated into the [Liverpool-York child head model (Dai et al. 2019)](https://doi.org/10.1007/s11263-019-01260-7) as it uses the same vertex order and IDs (starting from index 0). &nbsp;If you want to synthesize new models, take a look a the demo.py file. &nbsp;For information about the hierarchy in the h5-file, take a look at documentation.md.&lt;/p&gt

Impact of data synthesis strategies for the classification of craniosynostosis

IntroductionPhotogrammetric 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.MethodsWe 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.ResultsThe 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.ConclusionsWithout 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