Sven Hirsch

Topology and Hemodynamics of the Cortical Cerebrovascular System

The cerebrovascular system continuously delivers oxygen and energy substrates to the brain, which is one of the organs with the highest basal energy requirement in mammals. Discontinuities in the delivery lead to fatal consequences for the brain tissue. A detailed understanding of the structure of the cerebrovascular system is important for a multitude of (patho-)physiological cerebral processes and many noninvasive functional imaging methods rely on a signal that originates from the vasculature. Furthermore, neurodegenerative diseases often involve the cerebrovascular system and could contribute to neuronal loss. In this review, we focus on the cortical vascular system. In the first part, we present the current knowledge of the vascular anatomy. This is followed by a theory of topology and its application to vascular biology. We then discuss possible interactions between cerebral blood flow and vascular topology, before summarizing the existing body of the literature on quantitative cerebrovascular topology.

Improvements in Shape-From-Focus for Holographic Reconstructions With Regard to Focus Operators, Neighborhood-Size, and Height Value Interpolation

This paper presents a shape-from-focus method, which is improved with regard to the mathematical operator used for contrast measurement, the selection of the neighborhood size, surface refinement through interpolation, and surface postprocessing. Three-dimensional models of living human faces are presented with such a high resolution that single hairs are visible.

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters

CONTRIBUTIONS We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. EXPERIMENTS We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. RESULTS Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data.

Simulation of transcatheter aortic valve implantation under consideration of leaflet calcification

Transcatheter aortic valve implantation (TAVI) is a minimally invasive off-pump procedure to replace diseased aortic heart valves. Known complications include paravalvular leaks, atrioventricular blocks, coronary obstruction and annular rupture. Careful procedure planning including appropriate stent selection and sizing are crucial. Few patient-specific geometric parameters, like annular diameters, annular perimeter and measurement of the distance to the coronary ostia, are currently used within this process. Biomechanical simulation allows the consideration of extracted anatomy and material parameters for the intervention, which may improve planning and execution phases. We present a simulation workflow using a fully segmented aortic root anatomy, which was extracted from pre-operative CT-scan data and apply individual material models and parameters to predict the procedure outcome. Our results indicate the high relevance of calcification location and size for intervention planning, which are not sufficiently considered at this time. Our analysis can further provide guidance for accurate, patient-specific device positioning and future adaptations to stent design.

Scenario planning with integrated quantification – managing uncertainty in corporate strategy building

Purpose – This paper makes a case for the benefits of quantified scenarios as a foresight tool for strategic planning. First it aims to set the context of quantification approaches for strategic planning in foresight. Within world models, qualitative scenarios allow for a contingency perspective of the future, however their potential to be linked to strategic planning in corporate foresight is limited. In contrast to complex world models, forecasts on key indicators are easily applied to strategy processes, but lack the necessary capability to recognise uncertainty and decision points. The paper seeks to argue for a new participative and pragmatic approach in order to bridge the gap between the opposing approaches and aims to show how this quantification approach can be integrated with scenario construction on an operational level. Design/methodology/approach – The authors discuss a quantitative scenario process and argue for its suitability to corporate foresight. They then describe a range of leanings f...

Tissue Metabolism Driven Arterial Tree Generation

We present an approach to generate 3-D arterial tree models based on physiological principles while at the same time certain morphological properties are enforced at construction time. The driving force of the construction is a simplified angiogenesis model incorporating case-specific information about the metabolic demand within the considered domain. The vascular tree is constructed iteratively by successively adding new segments in chemotactic response to angiogenic growth factors secreted by ischemic cells. Morphometrically confirmed bifurcation statistics of vascular networks are incorporated to optimize the synthetic vasculature. The proposed method is able to generate artificial, yet physiologically plausible, arterial tree models that match the metabolic demand of the embedding tissue and fulfill the prescribed morphological properties at the same time. The proposed tree generation approach is applied in a simulation setup based on the metabolic configuration and anatomy of the macaque visual cortex. We analyze the generated tree models with respect to morphological and physiological aspects including fluid-dynamic simulations. The comparison of our results with the findings of different studies on the structure of cerebral vasculatures demonstrates the plausibility of our method.

Generation of digital textured surface models from hologram recordings

Digital sensors and fast digital image processing facilitate the use of pulsed holography for 3D surface measurement of moving objects. The real image of a hologram is reconstructed optically. A sequence of high-resolution projection images of the real image with a varying distance to the hologram is recorded digitally. Focus detection in this image sequence by digital image processing yields the shape of the recorded object. The image intensity serves as a precise pixel-matching texture. An application of this concept is the generation of a textured 3D computer model of a facial surface from a portrait hologram.

Oblique random forests for 3-d vessel detection using steerable filters and orthogonal subspace filtering

We propose a machine learning-based framework using oblique random forests for 3-D vessel segmentation. Two different kinds of features are compared. One is based on orthogonal subspace filtering where we learn 3-D eigenspace filters from local image patches that return task optimal feature responses. The other uses a specific set of steerable filters that show, qualitatively, similarities to the learned eigenspace filters, but also allow for explicit parametrization of scale and orientation that we formally generalize to the 3-D spatial context. In this way, steerable filters allow to efficiently compute oriented features along arbitrary directions in 3-D. The segmentation performance is evaluated on four 3-D imaging datasets of the murine visual cortex at a spatial resolution of 0.7μm. Our experiments show that the learning-based approach is able to significantly improve the segmentation compared to conventional Hessian-based methods. Features computed based on steerable filters prove to be superior to eigenfilter-based features for the considered datasets. We further demonstrate that random forests using oblique split directions outperform decision tree ensembles with univariate orthogonal splits.

Physiologically based construction of optimized 3-d arterial tree models

We present an approach to generate 3-D arterial tree models based on physiological principles while at the same time certain morphological properties are enforced at construction time in order to build individual vascular models down to the capillary level. The driving force of our approach is an angiogenesis model incorporating case-specific information about the metabolic activity in the considered domain. Additionally, we enforce morphometrically confirmed bifurcation statistics of vascular networks. The proposed method is able to generate artificial, yet physiologically plausible, arterial tree models that match the metabolic demand of the embedding tissue and fulfill the enforced morphological properties at the same time. We demonstrate the plausibility of our method on synthetic data for different metabolic configurations and analyze physiological and morphological properties of the generated tree models.

Holographic topometry for a dense visualization of soft tissue for facial reconstruction

Facial reconstruction can be used as a forensic technique to identify a person, when no other identification method is applicable. The facial soft tissue thickness applied to the skull is crucial when performing an accurate facial reconstruction. Historically, scientists developed several techniques to measure the soft tissue of the face. It was their aim, to build a database of a unique point-set, differentiated by gender, age, ethnic origin, BMI. All used a limited number of landmarks and an inaccurate measuring technique. We developed a contact-free and precise measuring technique, using low-dose CT and holographic data. Due to the extremely short exposure time, the holographic measurement is very precise. We lay out our first experiences to create a facial soft tissue layer map of the face.