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Polygonale Schädelmodelle bilden ein wichtiges Hilfsmittel für computergestützte Planungen im Bereich der plastischen Chirurgie. Wir beschreiben, wie derartige Modelle automatisch aus hochaufgelösten CT-Datensätzen erzeugt werden können. Durch einen lokal steuerbaren Simplifizierungsalgorithmus werden die Modelle so weit vereinfacht, daß auch auf kleineren Graphikcomputern interaktives Arbeiten möglich wird. Die Verwendung eines speziellen Transparenzmodells ermöglicht den ungehinderten Blick auf die bei der Planung relevanten Knochenstrukturen und läßt den Benutzer zugleich die Kopfumrisse des Patienten erkennen.
We present a unified approach for consistent remeshing of arbitrary non-manifold triangle meshes with additional user-defined feature lines, which together form a feature skeleton. Our method is based on local operations only and produces meshes of high regularity and triangle quality while preserving the geometry as well as topology of the feature skeleton and the input mesh.
Several non-invasive methods are common practice in natural sciences today. Here we present how they can be applied and contribute to current topics in cephalopod (paleo-) biology. Different methods will be compared in terms of time necessary to acquire the data, amount of data, accuracy/resolution, minimum/maximum size of objects that can be studied, the degree of post-processing needed and availability. The main application of the methods is seen in morphometry and volumetry of cephalopod shells. In particular we present a method for precise buoyancy calculation. Therefore, cephalopod shells were scanned together with different reference bodies, an approach developed in medical sciences. It is necessary to know the volume of the reference bodies, which should have similar absorption properties like the object of interest. Exact volumes can be obtained from surface scanning. Depending on the dimensions of the study object different computed tomography techniques were applied.
We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidian structure. A key advantage of our framework is that statistics in a manifold shape space become numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidian approach in terms of shape-based classification of morphological disorders.
We present an efficient GPU-based method to generate virtual X-ray images from tetrahedral meshes which are
associated with attenuation values. In addition, a novel approach is proposed that performs the model deformation
on the GPU. The tetrahedral grids are derived from volumetric statistical shape and intensity models (SSIMs) and describe anatomical structures. Our research targets at reconstructing 3D anatomical shapes by comparing virtual X-ray images generated using our novel approach with clinical data while varying the shape and density of the SSIM in an optimization process. We assume that a deformed SSIM adequately represents an anatomy of interest when the similarity between the virtual and the clinical X-ray image is maximized. The OpenGL implementation presented here generates accurate (virtual) X-ray images at interactive rates, thus qualifying it for its use in the reconstruction process.
We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on a complete 3D MRI scan. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, meniscal body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data or how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences.
We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patientspecific surface of the distal femur based on 2D Xray images. Our goal is to study from clinical data, how the platetobone distance affects bone healing. The patientspecific 3D shape of the femur is, however, seldom recorded for cases of femoral osteosynthesis since this typically requires Computed Tomography (CT), which comes at high cost and radiation dose. Our method instead utilizes two postoperative Xray images to derive the femoral shape and thus can be applied on radiographs that are taken in clinical routine for followup. First, the implant geometry is used as a calibration object to relate the implant and the individual Xray images spatially in a virtual Xray setup. In a second step, the patientspecific femoral shape and pose are reconstructed in the virtual setup by fitting a deformable statistical shape and intensity model (SSIM) to the images. The relative positioning between femur and implant is then assessed in terms of displacement between the reconstructed 3D shape of the femur and the plate. A preliminary evaluation based on 4 cadaver datasets shows that the method derives the platetobone distance with a mean absolute error of less than 1mm and a maximum error of 4.7 mm compared to ground truth from CT. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing.
3D Morphable Models (MM) are a popular tool for analysis and synthesis of facial expressions. They represent plausible variations in facial shape and appearance within a low-dimensional parameter space. Fitted to a face scan, the model's parameters compactly encode its expression patterns. This expression code can be used, for instance, as a feature in automatic facial expression recognition. For accurate classification, an MM that can adequately represent the various characteristic facets and variants of each expression is necessary. Currently available MMs are limited in the diversity of expression patterns. We present a novel high-quality Facial Expression Morphable Model built from a large-scale face database as a tool for expression analysis and synthesis. Establishment of accurate dense correspondence, up to finest skin features, enables a detailed statistical analysis of facial expressions. Various characteristic shape patterns are identified for each expression. The results of our analysis give rise to a new facial expression code. We demonstrate the advantages of such a code for the automatic recognition of expressions, and compare the accuracy of our classifier to state-of-the-art.
We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient-specific surface of the distal femur based on postoperative 2D radiographs. In a first step, the implant geometry is used as a calibration object to relate the implant and the individual X-ray images spatially in a virtual X-ray setup. Second, the patient-specific femoral shape and pose are reconstructed by fitting a deformable statistical shape and intensity model (SSIM) to the X-rays. The relative positioning between femur and implant is then assessed in terms of the displacement between the reconstructed 3D shape of the femur and the plate. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing and, ultimately, to derive load recommendations after surgery.