Anja Kuß

The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System

The honeybee standard brain (HSB) serves as an interactive tool for relating morphologies of bee brain neurons and provides a reference system for functional and bibliographical properties (http://www.neurobiologie.fu-berlin.de/beebrain/). The ultimate goal is to document not only the morphological network properties of neurons collected from separate brains, but also to establish a graphical user interface for a neuron-related data base. Here, we review the current methods and protocols used to incorporate neuronal reconstructions into the HSB. Our registration protocol consists of two separate steps applied to imaging data from two-channel confocal microscopy scans: (1) The reconstruction of the neuron, facilitated by an automatic extraction of the neurons skeleton based on threshold segmentation, and (2) the semi-automatic 3D segmentation of the neuropils and their registration with the HSB. The integration of neurons in the HSB is performed by applying the transformation computed in step (2) to the reconstructed neurons of step (1). The most critical issue of this protocol in terms of user interaction time – the segmentation process – is drastically improved by the use of a model-based segmentation process. Furthermore, the underlying statistical shape models (SSM) allow the visualization and analysis of characteristic variations in large sets of bee brain data. The anatomy of neural networks composed of multiple neurons that are registered into the HSB are visualized by depicting the 3D reconstructions together with semantic information with the objective to integrate data from multiple sources (electrophysiology, imaging, immunocytochemistry, molecular biology). Ultimately, this will allow the user to specify cell types and retrieve their morphologies along with physiological characterizations.

Integrated visualization of morphologic and perfusion data for the analysis of coronary artery disease

We present static and dynamic techniques to visualize perfusion data and to relate perfusion data to morphologic image data. In particular, we describe the integrated analysis of MRI myocardial perfusion data with CT coronary angiographies depicting the morphology. We refined the Bulls-Eye Plot, a wide-spread and accepted analysis tool in cardiac diagnosis, to show aggregated information of perfusion data at rest and under stress. The correlation between regions of the myocard with reduced perfusion and 3d renditions of the coronary vessels can be explored within a synchronized visualization of both. With our research, we attempt to improve the diagnosis of early stage coronary artery disease.

Ontology-based visualization of hierarchical neuroanatomical structures

This work presents a method for generating intuitive visualizations for high-level user queries to an hierarchical surface-based neuroanatomical atlas. We combine a spreading activation approach for computing focus and context in an ontology with a specific level-of-detail strategy for hierarchical structures. We demonstrate our method on an atlas of the bee brain.

The HoneyBee Standard Brain (HSB) – a versatile atlas tool for integrating data and data exchange in the neuroscience community

The HoneyBee Standard Brain (HSB) serves as an interactive tool for comparing morphologies of bee brain neurons and relates it to functional as well as biological properties [1]. Recent efforts by several labs have accumulated confocal image stacks from extraand intracellular stained neurons in the bee central nervous system [2]. We present a pipeline through which confocal images of neurons can be traced and presented in a common space (Figure 1). The first step is an automatic extraction of the neurons skeleton based on threshold segmentation. In a second step this skeleton can be edited using semi-automatic and interactive tools within Amiras Filament Editor. Hereby, the user is assisted by displaying maximum intensity projections and 3D representations in two separate viewers. Next the skeletonized neuron can be labeled (i.e. annotated) by using multiple sets of hierarchically organized label attributes (Figure 2). Finally, the neurons topological and metric features can be visualized, statistically analyzed and/or exported to a simulation package such as Neuron.

Integrierte Visualisierung von Anatomie und Perfusion des Myokards zur Früherkennung der Koronaren Herzkrankheit

Wir beschreiben die integrierte Visualisierung von anatomischen Daten des linken Ventrikels und der Koronargefase mit Ruhe/Stress Perfusionsdaten des Myokards. Der anatomische Zusammenhang zwischen Myokardregionen in den Perfusionsdaten und den versorgenden Abschnitten der Koronargefase wird durch eine interaktive 3d-Visualisierung illustriert. Diese kann im Rahmen der Fruherkennung der Koronaren Herzkrankheit fur die Detektion hamodynamisch relevanter Stenosen genutzt werden. Weiterhin unterstutzen die integrierte Visualisierung korrespondierender Parameter der Kontrastmittelanflutung und -auswaschung basierend auf Ruhe/Stress Perfusionsdaten und die automatische Hervorhebung signifikanter Unterschiede den Ruhe/Stress Vergleich und erleichtern die Detektion minderperfundierter Regionen.