Data-Driven Classification Methods for Craniosynostosis Using 3D Surface Scans

Diese Arbeit befasst sich mit strahlungsfreier Klassifizierung von Kraniosynostose mit zusätzlichem Schwerpunkt auf Datenaugmentierung und auf die Verwendung synthetischer Daten als Ersatz für klinische Daten. Motivation: Kraniosynostose ist eine Erkrankung, die Säuglinge betrifft und zu Kopfdeformitäten führt. Diagnose mittels strahlungsfreier 3D Oberflächenscans ist eine vielversprechende Alternative zu traditioneller computertomographischer Bildgebung. Aufgrund der niedrigen Prävalenz und schwieriger Anonymisierbarkeit sind klinische Daten nur spärlich vorhanden. Diese Arbeit adressiert diese Herausforderungen, indem sie neue Klassifizierungsalgorithmen vorschlägt, synthetische Daten für die wissenschaftliche Gemeinschaft erstellt und zeigt, dass es möglich ist, klinische Daten vollständig durch synthetische Daten zu ersetzen, ohne die Klassifikationsleistung zu beeinträchtigen. Methoden: Ein Statistisches Shape Modell (SSM) von Kraniosynostosepatienten wird erstellt und öffentlich zugänglich gemacht. Es wird eine 3D-2D-Konvertierung von der 3D-Gittergeometrie in ein 2D-Bild vorgeschlagen, die die Verwendung von Convolutional Neural Networks (CNNs) und Datenaugmentierung im Bildbereich ermöglicht. Drei Klassifizierungsansätze (basierend auf cephalometrischen Messungen, basierend auf dem SSM, und basierend auf den 2D Bildern mit einem CNN) zur Unterscheidung zwischen drei Pathologien und einer Kontrollgruppe werden vorgeschlagen und bewertet. Schließlich werden die klinischen Trainingsdaten vollständig durch synthetische Daten aus einem SSM und einem generativen adversarialen Netz (GAN) ersetzt. Ergebnisse: Die vorgeschlagene CNN-Klassifikation übertraf konkurrierende Ansätze in einem klinischen Datensatz von 496 Probanden und erreichte einen F1-Score von 0,964. Datenaugmentierung erhöhte den F1-Score auf 0,975. Zuschreibungen der Klassifizierungsentscheidung zeigten hohe Amplituden an Teilen des Kopfes, die mit Kraniosynostose in Verbindung stehen. Das Ersetzen der klinischen Daten durch synthetische Daten, die mit einem SSM und einem GAN erstellt wurden, ergab noch immer einen F1-Score von über 0,95, ohne dass das Modell ein einziges klinisches Subjekt gesehen hatte. Schlussfolgerung: Die vorgeschlagene Umwandlung von 3D-Geometrie in ein 2D-kodiertes Bild verbesserte die Leistung bestehender Klassifikatoren und ermöglichte eine Datenaugmentierung während des Trainings. Unter Verwendung eines SSM und eines GANs konnten klinische Trainingsdaten durch synthetische Daten ersetzt werden. Diese Arbeit verbessert bestehende diagnostische Ansätze auf strahlungsfreien Aufnahmen und demonstriert die Verwendbarkeit von synthetischen Daten, was klinische Anwendungen objektiver, interpretierbarer, und weniger kostspielig machen

A Statistical Shape Model Pipeline to Enable the Creation of Synthetic 3D Liver Data

The application of machine learning approaches in medical technology is gaining more and more attention. Due to the high restrictions for collecting intraoperative patient data, synthetic data is increasingly used to support the training of artificial neural networks. We present a pipeline to create a statistical shape model (SSM) using 28 segmented clinical liver CT scans. Our pipeline consists of four steps: data preprocessing, rigid alignment, template morphing, and statistical modeling. We compared two different template morphing approaches: Laplace-Beltrami-regularized projection (LBRP) and nonrigid iterative closest points translational (N-ICP-T) and evaluated both morphing approaches and their corresponding shape model performance using six metrics. LBRP achieved a smaller mean vertex-to-nearest-neighbor distances (2.486 ± 0.897 mm) than N-ICP-T (5.559 ± 2.413 mm). Generalization and specificity errors for LBRP were consistently lower than those of N-ICP-T. The first principal components of the SSM showed realistic anatomical ariations. The performance of the SSM was comparable to a state-of-the-art model

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

3D-Guided Face Manipulation of 2D Images for the Prediction of Post-Operative Outcome after Cranio-Maxillofacial Surgery

Cranio-maxillofacial surgery often alters the aesthetics of the face which can be a heavy burden for patients to decide whether or not to undergo surgery. Today, physicians can predict the post-operative face using surgery planning tools to support the patient\u27s decision-making. While these planning tools allow a simulation of the post-operative face, the facial texture must usually be captured by another 3D texture scan and subsequently mapped on the simulated face. This approach often results in face predictions that do not appear realistic or lively looking and are therefore ill-suited to guide the patient\u27s decision-making. Instead, we propose a method using a generative adversarial network to modify a facial image according to a 3D soft-tissue estimation of the post-operative face. To circumvent the lack of available data pairs between pre- and post-operative measurements we propose a semi-supervised training strategy using cycle losses that only requires paired open-source data of images and 3D surfaces of the face\u27s shape. After training on "in-the-wild" images we show that our model can realistically manipulate local regions of a face in a 2D image based on a modified 3D shape. We then test our model on four clinical examples where we predict the post-operative face according to a 3D soft-tissue prediction of surgery outcome, which was simulated by a surgery planning tool. As a result, we aim to demonstrate the potential of our approach to predict realistic post-operative images of faces without the need of paired clinical data, physical models, or 3D texture scans

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

The Use of Artificial Intelligence for the Classification of Craniofacial Deformities

Positional cranial deformities are a common finding in toddlers, yet differentiation from craniosynostosis can be challenging. The aim of this study was to train convolutional neural networks (CNNs) to classify craniofacial deformities based on 2D images generated using photogrammetry as a radiation-free imaging technique. A total of 487 patients with photogrammetry scans were included in this retrospective cohort study: children with craniosynostosis (n = 227), positional deformities (n = 206), and healthy children (n = 54). Three two-dimensional images were extracted from each photogrammetry scan. The datasets were divided into training, validation, and test sets. During the training, fine-tuned ResNet-152s were utilized. The performance was quantified using tenfold cross-validation. For the detection of craniosynostosis, sensitivity was at 0.94 with a specificity of 0.85. Regarding the differentiation of the five existing classes (trigonocephaly, scaphocephaly, positional plagiocephaly left, positional plagiocephaly right, and healthy), sensitivity ranged from 0.45 (positional plagiocephaly left) to 0.95 (scaphocephaly) and specificity ranged from 0.87 (positional plagiocephaly right) to 0.97 (scaphocephaly). We present a CNN-based approach to classify craniofacial deformities on two-dimensional images with promising results. A larger dataset would be required to identify rarer forms of craniosynostosis as well. The chosen 2D approach enables future applications for digital cameras or smartphones

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

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 Pipeline to Enable the Creation of Synthetic 3D Liver Data

The application of machine learning approaches in medical technology is gaining more and more attention. Due to the high restrictions for collecting intraoperative patient data, synthetic data is increasingly used to support the training of artificial neural networks. We present a pipeline to create a statistical shape model (SSM) using 28 segmented clinical liver CT scans. Our pipeline consists of four steps: data preprocessing, rigid alignment, template morphing, and statistical modeling. We compared two different template morphing approaches: Laplace-Beltrami-regularized projection (LBRP) and nonrigid iterative closest points translational (N-ICP-T) and evaluated both morphing approaches and their corresponding shape model performance using six metrics. LBRP achieved a smaller mean vertex-to-nearest-neighbor distances (2.486±0.897 mm) than N-ICP-T (5.559±2.413 mm). Generalization and specificity errors for LBRP were consistently lower than those of N-ICP-T. The first principal components of the SSM showed realistic anatomical variations. The performance of the SSM was comparable to a state-of-the-art model

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