Aaron Scherzinger

Automated Segmentation of Immunostained Cell Nuclei in 3D Ultramicroscopy Images

Detection, segmentation, and quantification of individual cell nuclei is a standard task in biomedical applications. Due to the increasing volume of acquired image data, it is not possible to rely on manual labeling and object counting. Instead, automated image processing methods have to be applied. Especially in three-dimensional data, one of the major challenges is the separation of touching cell nuclei in densely packed clusters. In this paper, we propose a method for automated detection and segmentation of immunostained cell nuclei in ultramicroscopy images. Our algorithm utilizes interactive learning and voxel classification to obtain a foreground segmentation and subsequently performs the splitting process for each cluster using a multi-step watershed approach. We have evaluated our results using reference images manually labeled by domain experts and compare our approach to state-of-the art methods.

SkInteract: An On-body Interaction System Based on Skin-Texture Recognition

In this paper we propose SkInteract, a system for on-body interaction utilizing the diverse texture of the human skin. We use an area fingerprint sensor to capture images and locate the corresponding area within a previously created map of the skin surface. In addition to the location of the sensor it is possible to calculate its orientation with respect to the reference map. This allows to assign arbitrary semantics to areas of the user’s skin and to use the rotation as an additional input modality. In order to evaluate the feasibility of SkInteract a user study with a preliminary prototype was conducted. We propose two different interaction concepts which are based on either attaching a fixed sensor to a wearable device or using a moveable sensor, for instance attached to a pen, to perform on-body input.

CNN-Based Background Subtraction for Long-Term In-Vial FIM Imaging

In recent years, the importance of behavioral studies of model organisms such as Drosophila melanogaster has significantly increased in biological research. Recently, a novel monitoring setup for analyzing Drosophila larvae in culture vials was proposed which allows researchers to conduct long-term studies without disturbing the animals’ behavioral routine. However, when monitoring larvae in such a setup over several days, dirt accumulates on the vial surface, leading to artifacts in the segmentation process. To overcome this problem and enable researchers to perform experiments involving long-term tracking of the animals, we propose a method for background subtraction which is based on convolutional neural networks (CNNs). Our method produces good results and significantly outperforms other methods. In addition, we show that besides its good performance our compact CNN architecture allows us to apply our method for online-processing on microcomputers in real-time.

VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations

BACKGROUND Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections. METHODS VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy-based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin. RESULTS Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%). CONCLUSION VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin. FUNDING Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003.

Interactive Exploration of Cosmological Dark-Matter Simulation Data

The winning entry of the 2015 IEEE Scientific Visualization Contest, this article describes a visualization tool for cosmological data resulting from dark-matter simulations. The proposed system helps users explore all aspects of the data at once and receive more detailed information about structures of interest at any time. Moreover, novel methods for visualizing and interactively exploring dark-matter halo substructures are proposed.

Interactive position-dependent customization of transfer function classification parameters in volume rendering

In direct volume rendering (DVR) and related techniques a basic operation is the classification of data values by mapping (mostly scalar) intensities to color values using a transfer function. However, in some cases this kind of mapping might not suffice to achieve satisfying rendering results, for instance when intensity homogeneities occur in the volume data due to technical restrictions of the scanner technology. In this case it might be desirable to customize one or more parameters of the visualization depending on the position within the volume. In this paper we propose a novel approach for an interactive position-dependent customization of arbitrary parameters of the transfer function classification. Our method can easily be integrated into existing volume rendering pipelines by incorporating an additional operation during the classification step. It allows the user to interactively modify the rendering result by specifying reference points within the data set and customizing their associated visualization parameters while receiving direct visual feedback. Since the additional memory requirements of our method do not depend on the size of the visualized data our approach is applicable to large data sets, for instance in the field of ultra microscopy.

Three-Dimensional Visualization of the Lymphatic Vasculature

Like the circulatory blood vessel system, the dendriform lymphatic vascular system forms a disseminated organ that is virtually indispensible for the function of most other organs. Formation and maintenance of the correct topology are essential for lymph vessel physiology and hence analysis of its three-dimensional architecture provides crucial functional information.Here we describe protocols for whole-mount immunostaining of the vessel systems in various mouse tissues, mouse fetuses, and human skin biopsies. The resulting samples are suited after flat mounting for confocal microscopy or after optical tissue clearing for light sheet microscopy. Both microscopic modalities use optical sectioning to generate image stacks from which the three-dimensional vessel structure can be digitally reconstructed. We introduce the open software package Voreen, developed at the University of Münster. Voreen has been adapted and extended for the interactive visualization of large multichannel image stacks on commodity hardware, allowing for a faithful digital representation of the spatial structure of the vessel systems in whole-mount stained tissue samples.

Automatic non-invasive heartbeat quantification of Drosophila pupae

The importance of studying model organisms such as Drosophila melanogaster has significantly increased in recent biological research. Amongst others, Drosophila can be used to study heart development and heartbeat related diseases. Here we propose a method for automatic in vivo heartbeat detection of Drosophila melanogaster pupae based on morphological structures which are recorded without any dissection using FIM imaging. Our approach is easy-to-use, has low computational costs, and enables high-throughput experiments. After automatically segmenting the heart region of the pupa in an image sequence, the heartbeat is indirectly determined based on intensity variation analysis. We have evaluated our method using 47,631 manually annotated frames from 29 image sequences recorded with different temporal and spatial resolutions which are made publicly available. We show that our algorithm is both precise since it detects more than 95% of the heartbeats correctly as well as robust since the same standardized set of parameters can be used for all sequences. The combination of FIM imaging and our algorithm enables a reliable heartbeat detection of multiple Drosophila pupae while simultaneously avoiding any time consuming preparation of the animals.

An Efficient Geometric Algorithm for Clipping and Capping Solid Triangle Meshes.

Clipping three-dimensional geometry by arbitrarily oriented planes is a common operation in computer graphics and visualization applications. In most cases, the geometry used in those applications is provided as surface models consisting of triangles which are called meshes. Clipping such surface models by a plane cuts them open, destroying the illusion of a solid object. Often this is not desirable, and the resulting mesh should again be a closed surface model, e.g., when generating cross-sections in technical visualization applications. We propose an algorithm which performs the clipping operation geometrically for a given input mesh on the GPU. The intersection edges of the mesh and the clipping plane are then transferred to the CPU, where a cap geometry closing the mesh is computed and eventually added to the clipped mesh. Our algorithm can process solid (i.e., closed two-manifold) triangle meshes, or sets of non-intersecting solids, and has a worst-case runtime of O(N +n logn) where N is the number of triangles in the input geometry, and n is the number of input triangles intersecting the clipping plane.

An Enhanced Multi-label Random Walk for Biomedical Image Segmentation Using Statistical Seed Generation

Image segmentation is one of the fundamental problems in biomedical applications and is often mandatory for quantitative analysis in life sciences. In recent years, the amount of biomedical image data has significantly increased, rendering manual segmentation approaches impractical for large-scale studies. In many cases, the use of semi-automated techniques is convenient, as those approaches allow to incorporate domain knowledge of experts into the segmentation process. The random walker framework is among the most popular semi-automated segmentation algorithms, as it can easily be applied to multi-label situations. However, this method usually requires manual input on each individual image and, even worse, for each disconnected object. This is problematic for segmenting multiple unconnected objects like individual cells, or very fine anatomical structures. Here, we propose a seed generation scheme as an extension to the random walker framework. Our method needs only few manual labels to generate a sufficient number of seeds for reliably segmenting multiple objects of interest, or even a series of images or videos from an experiment. We show that our method is robust against parameter settings and evaluate the performance on both synthetic as well as real-world biomedical image data.