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

Polygon Meshes Reconstruction

Práce se zabývá problematikou statistického zpracování polygonálních modelů. Cílem diplomové práce je rekonstrukce polygonálního modelu poškozené lebky. Rekonstrukce je řešena pomocí statistického modelu tvaru lebky. Práce pokrývá problém registrace lebek založené na metodě thin-plate spline, zarovnání modelů obecnou prokrústovskou analýzou, identi fikaci chybějících částí lebky jako odlehlých hodnot statistického modelu. Následně práce řeší doplnění chybějících částí lebky a ověření věrohodnosti doplněných částí.The thesis is focussed on the reconstruction of a damaged skull represented by a polygonal model. The reconstruction is based on a statistical shape model of the skull. The thesis covers the registration of skulls by using a thin-plate spline method, aligning polygonal models by generalized procrustes analysis, the identification of missing parts of a skull by means of statistical shape models outliers analysis. Finally, missing parts of the skull are reconstructed and the accuracy of the reconstruction is estimated.

A machine learning approach to statistical shape models with applications to medical image analysis

Statistical shape models have become an indispensable tool for image analysis. The use of shape models is especially popular in computer vision and medical image analysis, where they were incorporated as a prior into a wide range of different algorithms. In spite of their big success, the study of statistical shape models has not received much attention in recent years. Shape models are often seen as an isolated technique, which merely consists of applying Principal Component Analysis to a set of example data sets. In this thesis we revisit statistical shape models and discuss their construction and applications from the perspective of machine learning and kernel methods. The shapes that belong to an object class are modeled as a Gaussian Process whose parameters are estimated from example data. This formulation puts statistical shape models in a much wider context and makes the powerful inference tools from learning theory applicable to shape modeling. Furthermore, the formulation is continuous and thus helps to avoid discretization issues, which often arise with discrete models. An important step in building statistical shape models is to establish surface correspondence. We discuss an approach which is based on kernel methods. This formulation allows us to integrate the statistical shape model as an additional prior. It thus unifies the methods of registration and shape model fitting. Using Gaussian Process regression we can integrate shape constraints in our model. These constraints can be used to enforce landmark matching in the fitting or correspondence problem. The same technique also leads directly to a new solution for shape reconstruction from partial data. In addition to experiments on synthetic 2D data sets, we show the applicability of our methods on real 3D medical data of the human head. In particular, we build a 3D model of the human skull, and present its applications for the planning of cranio-facial surgeries

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

The Probabilistic Active Shape Model: From Model Construction to Flexible Medical Image Segmentation

Automatic processing of three-dimensional image data acquired with computed tomography or magnetic resonance imaging plays an increasingly important role in medicine. For example, the automatic segmentation of anatomical structures in tomographic images allows to generate three-dimensional visualizations of a patient’s anatomy and thereby supports surgeons during planning of various kinds of surgeries. Because organs in medical images often exhibit a low contrast to adjacent structures, and because the image quality may be hampered by noise or other image acquisition artifacts, the development of segmentation algorithms that are both robust and accurate is very challenging. In order to increase the robustness, the use of model-based algorithms is mandatory, as for example algorithms that incorporate prior knowledge about an organ’s shape into the segmentation process. Recent research has proven that Statistical Shape Models are especially appropriate for robust medical image segmentation. In these models, the typical shape of an organ is learned from a set of training examples. However, Statistical Shape Models have two major disadvantages: The construction of the models is relatively difficult, and the models are often used too restrictively, such that the resulting segmentation does not delineate the organ exactly. This thesis addresses both problems: The first part of the thesis introduces new methods for establishing correspondence between training shapes, which is a necessary prerequisite for shape model learning. The developed methods include consistent parameterization algorithms for organs with spherical and genus 1 topology, as well as a nonrigid mesh registration algorithm for shapes with arbitrary topology. The second part of the thesis presents a new shape model-based segmentation algorithm that allows for an accurate delineation of organs. In contrast to existing approaches, it is possible to integrate not only linear shape models into the algorithm, but also nonlinear shape models, which allow for a more specific description of an organ’s shape variation. The proposed segmentation algorithm is evaluated in three applications to medical image data: Liver and vertebra segmentation in contrast-enhanced computed tomography scans, and prostate segmentation in magnetic resonance images

Building Shape Models from Lousy Data

Statistical shape models have gained widespread use in medical imageanalysis. In order for such models to be statistically meaningful, a large number ofdata sets have to be included. The number of available data sets is usually limitedand often the data is corrupted by imaging artifacts or missing information. Wepropose a method for building a statistical shape model from such ”lousy” datasets. The method works by identifying the corrupted parts of a shape as statisticaloutliers and excluding these parts from the model. Only the parts of a shape thatwere identified as outliers are discarded, while all the intact parts are includedin the model. The model building is then performed using the EM algorithm forprobabilistic principal component analysis, which allows for a principled wayto handle missing data. Our experiments on 2D synthetic and real 3D medicaldata sets confirm the feasibility of the approach. We show that it yields superiormodels compared to approaches using robust statistics, which only downweightthe influence of outliers