2D Vessel Segmentation Using Local Adaptive Contrast Enhancement - Schuldhaus, Dominik; Spiegel, Martin; Redel, Thomas; Polyanskaya, Maria; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin, 2011)

A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data - Spiegel, Martin; Redel, T.; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Physics in Medicine and Biology, 2011)
Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling. © 2011 Institute of Physics and Engineering in Medicine.

Classification-based summation of cerebral digital subtraction angiography series for image post-processing algorithms - Schuldhaus, Dominik; Spiegel, Martin; Redel, T.; Polyanskaya, Maria; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Physics in Medicine and Biology, 2011)
X-ray-based 2D digital subtraction angiography (DSA) plays a major role in the diagnosis, treatment planning and assessment of cerebrovascular disease, i.e. aneurysms, arteriovenous malformations and intracranial stenosis. DSA information is increasingly used for secondary image post-processing such as vessel segmentation, registration and comparison to hemodynamic calculation using computational fluid dynamics. Depending on the amount of injected contrast agent and the duration of injection, these DSA series may not exhibit one single DSA image showing the entire vessel tree. The interesting information for these algorithms, however, is usually depicted within a few images. If these images would be combined into one image the complexity of segmentation or registration methods using DSA series would drastically decrease. In this paper, we propose a novel method automatically splitting a DSA series into three parts, i.e. mask, arterial and parenchymal phase, to provide one final image showing all important vessels with less noise and moving artifacts. This final image covers all arterial phase images, either by image summation or by taking the minimum intensities. The phase classification is done by a two-step approach. The mask/arterial phase border is determined by a Perceptron-based method trained from a set of DSA series. The arterial/parenchymal phase border is specified by a threshold-based method. The evaluation of the proposed method is two-sided: (1) comparison between automatic and medical expert-based phase selection and (2) the quality of the final image is measured by gradient magnitudes inside the vessels and signal-to-noise (SNR) outside. Experimental results show a match between expert and automatic phase separation of 93%/50% and an average SNR increase of up to 182% compared to summing up the entire series. © 2011 Institute of Physics and Engineering in Medicine.

Pattern Recognition in Medical and Health Engineering '10 - Hornegger, Joachim; Höller, Kurt Emmerich; Spiegel, Martin; Niedermeier, Hans-Peter (Kloster Banz, 2010)
UnivIS-Import:2015-05-08:Pub.2010.tech.IMMD.IMMD5.rectif

Segmentation of Cerebral Vasculature - Alicioglu, Yesim; Spiegel, Martin; Wimmer, Andreas; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration - Spiegel, Martin; Hahn, Dieter; Daum, Volker; Wasza, Jakob; Hornegger, Joachim (Computerized Medical Imaging and Graphics, 2009)
Active shape models (ASMs) are widely used for applications in the field of image segmentation. Building an ASM requires to determine point correspondences for input training data, which usually results in a set of landmarks distributed according to the statistical variations. State-of-the-art methods solve this problem by minimizing the description length of all landmarks using a parametric mapping of the target shape (e.g. a sphere). In case of models composed of multiple sub-parts or highly non-convex shapes, these techniques feature substantial drawbacks. This article proposes a novel technique for solving the crucial correspondence problem using non-rigid image registration. Unlike existing approaches the new method yields more detailed ASMs and does not require explicit or parametric formulations of the problem. Compared to other methods, the already built ASM can be updated with additional prior knowledge in a very efficient manner. For this work, a training set of 3-D kidney pairs has been manually segmented from 41 CT images of different patients and forms the basis for a clinical evaluation. The novel registration based approach is compared to an already established algorithm that uses a minimum description length (MDL) formulation. The presented results indicate that the use of non-rigid image registration to solve the point correspondence problem leads to improved ASMs and more accurate segmentation results. The sensitivity could be increased by approximately 10%. Experiments to analyze the dependency on the user initialization also show a higher sensitivity of 5-15%. The mean squared error of the segmentation results and the ground truth manually classified data could also be reduced by 20-34% with respect to varying numbers of training samples. © 2008 Elsevier Ltd. All rights reserved.

Technical feasibility of 2D-3D coregistration for visualization of self-expandable microstents to facilitate coil embolization of broad-based intracranial aneurysms: An in vitro study - Richter, Gregor; Pfister, Marcus; Struffert, Tobias; Engelhorn, Tobias; Dölken, Marc; Spiegel, Martin; Hornegger, Joachim; Dörfler, Arnd (Neuroradiology, 2009)
Introduction: The use of self-expandable microstents for treatment of broad-based intracranial aneurysms is widely spread. However, poor fluoroscopic visibility of the stents remains disadvantageous during the coiling procedure. Flat detector angiographic computed tomography (ACT) provides high resolution imaging of microstents even though integration of this imaging modality in the neurointerventional workflow has not been widely reported. Methods: An acrylic glass model was used to simulate the situation of a broad-based sidewall aneurysm. After insertion of a self-expandable microstent, ACT was performed. The resulting 3D dataset of the Microstent was subsequently projected into a conventional 2D fluoroscopic roadmap. This 3D visualization of the stent supported the coil embolization procedure of the in vitro aneurysm. Results: In vitro 2D-3D coregistration with integration of 3D ACT data of a self-expandable microstent in a conventional 2D roadmap is feasible. Conclusions: Unsatisfying stent visibility constrains clinical cases with complex parent vessel anatomy and challenging aneurysm geometry; hence, this technique potentially may be useful in such cases. In our opinion, the clinical feasibility and utility of this new technique should be verified in a clinical aneurysm embolization study series using 2D-3D coregistration. © 2009 Springer-Verlag.

Tetrahedral and polyhedral mesh evaluation for cerebral hemodynamic simulation–a comparison - Spiegel, Martin; Redel, Thomas; Zhang, Yao; Struffert, Tobias; Hornegger, Joachim; Grossman, Robert G.; Dörfler, Arnd; Karmonik, Christof (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2009)
Computational fluid dynamic (CFD) based on patient-specific medical imaging data has found widespread use for visualizing and quantifying hemodynamics in cerebrovascular disease such as cerebral aneurysms or stenotic vessels. This paper focuses on optimizing mesh parameters for CFD simulation of cerebral aneurysms. Valid blood flow simulations strongly depend on the mesh quality. Meshes with a coarse spatial resolution may lead to an inaccurate flow pattern. Meshes with a large number of elements will result in unnecessarily high computation time which is undesirable should CFD be used for planning in the interventional setting. Most CFD simulations reported for these vascular pathologies have used tetrahedral meshes. We illustrate the use of polyhedral volume elements in comparison to tetrahedral meshing on two different geometries, a sidewall aneurysm of the internal carotid artery and a basilar bifurcation aneurysm. The spatial mesh resolution ranges between 5,119 and 228,118 volume elements. The evaluation of the different meshes was based on the wall shear stress previously identified as a one possible parameter for assessing aneurysm growth. Polyhedral meshes showed better accuracy, lower memory demand, shorter computational speed and faster convergence behavior (on average 369 iterations less).
UnivIS-Import:2017-01-09:Pub.2009.tech.IMMD.IMMD5.tetrah_3

Tetrahedral vs. polyhedral mesh size evaluation on flow velocity and wall shear stress for cerebral hemodynamic simulation - Spiegel, Martin; Redel, Thomas; Zhang, Y. Jonathan; Struffert, Tobias; Hornegger, Joachim; Grossman, Robert G.; Dörfler, Arnd; Karmonik, Christof (Computer Methods in Biomechanics and Biomedical Engineering, 2011)
Haemodynamic factors, in particular wall shear stresses (WSSs) may have significant impact on growth and rupture of cerebral aneurysms. Without a means to measure WSS reliably in vivo, computational fluid dynamic (CFD) simulations are frequently employed to visualise and quantify blood flow from patient-specific computational models. With increasing interest in integrating these CFD simulations into pretreatment planning, a better understanding of the validity of the calculations in respect to computation parameters such as volume element type, mesh size and mesh composition is needed. In this study, CFD results for the two most common aneurysm types (saccular and terminal) are compared for polyhedral- vs. tetrahedral-based meshes and discussed regarding future clinical applications. For this purpose, a set of models were constructed for each aneurysm with spatially varying surface and volume mesh configurations (mesh size range: 5119-258, 481 volume elements). WSS distribution on the model wall and point-based velocity measurements were compared for each configuration model. Our results indicate a benefit of polyhedral meshes in respect to convergence speed and more homogeneous WSS patterns. Computational variations of WSS values and blood velocities are between 0.84 and 6.3% from the most simple mesh (tetrahedral elements only) and the most advanced mesh design investigated (polyhedral mesh with boundary layer). © 2011 Taylor & Francis.

Towards Real-time Guidewire Detection and Tracking in the Field of Neuroradiology - Spiegel, Martin; Pfister, Markus; Hahn, Dieter; Daum, Volker; Hornegger, Joachim; Struffert, Tobias; Dörfler, Arnd (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
Two-dimensional roadmapping is considered state-of-the-art in guidewire navigation during endovascular interventions. This paper presents a methodology for extracting the guidewire from a sequence of 2-D roadmap images in almost real time. The detected guidewire can be used to improve its visibility on noisy fluoroscopic images or to do a back projection of the guidewire into a registered 3-D vessel tree. A lineness filter based on the Hessian matrix is used to detect only those line structures in the image that lie within the vessel tree. Loose wire fragments are properly linked by a novel connection method fulfilling clinical processing requirements. We show that Dijkstra's algorithm can be applied to efficiently compute the optimal connection path. The entire guidewire is finally approximated by a B-spline curve in a least-squares manner. The proposed method is both integrated into a commercial clinical prototype and evaluated on five different patient data sets containing up to 249 frames per image series. © 2009 SPIE.