Don’t miss the complete programme of VCBM 2018!
Annotated dendrograms for neurons from the larval fruit fly brain.
Full paper
Recent advances in neuroscience have made it possible to reconstruct all neurons of an entire neural circuit in the larval fruit fly brain from serial electron microscopy image stacks. The reconstructed neurons are morphologically complex 3D graphs whose nodes are annotated with labels representing different types of synapses. Here, we propose a method to draw simplified, yet realistic 2D neuron sketches of insect neurons in order to help biologists formulate hypotheses on neural function at the microcircuit level. The sketches are dendrograms that capture a neuron’s branching structure and that preserve branch lengths, providing realistic estimates for distances and signal travel times between synapses. To improve readability of the often densely clustered synapse annotations, synapses are automatically summarized in local clusters of synapses of the same type and arranged to minimize label overlap. We show that two major neuron classes of an olfactory circuit in the larval fruit fly brain can be discriminated visually based on the dendrograms. Unsupervised and supervised data analysis reveals that class discrimination can be performed using morphological features derived from the dendrograms.
A Multilinear Model for Bidirectional Craniofacial Reconstruction
Full Paper
In this paper, we present a bidirectional facial reconstruction method, i.e., a method for estimating the skull given a skin surface scan and vice versa estimating the skin surface given a skull scan. Our approach is based on a multilinear model which describes the correlation between the skull structure and facial soft tissue thickness (FSTT) on the one hand and the head/face surface geometry on the other hand. Training this model requires to densely sample the tensor product space of skull identity times FSTT variation. This data cannot be obtained by measurements alone. We generate it by enriching measured data — volumetric computed tomography (CT) scans and 3D surface scans of the head — by simulating statistically plausible FSTT variations. We demonstrate the versatility of our novel multilinear model by estimating faces from given skulls as well as skulls from given faces with an approximation error below three millimeters. Thanks to its multilinear nature fitting takes just a couple of seconds. To foster further research in this direction we will make our multilinear model publicly available.
Iterative Exploration of Big Brain Network Data
Full paper
A current quest in neurscience is the understanding of how genes, structure and behavior relate to one another. In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data – brain-network graphs with billions edges – whose analysis for higher order relations in structural or functional neuroanatomy together with genetic data may reveal novel insights into brain functionality. This creates a need for joint exploration of spatial data, such as gene expression patterns, whole brain gene co-expression correlation, structural and functional connectivities together with neuroanatomical parcellations. Current experimental workflows involve time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. We propose a web-based framework to explore heterogeneous neurobiological data in an integrated visual analytics workflow. On-demand queries on volumetric gene expression and connectivity data enable an interactive dissection of dense network graphs with of billion-edges on voxel-resolution in real-time, and based on their spatial context. The queries can be executed in a cascading way, to take higher order connections between brain regions into account. Relating data to the hierarchical organization of common anatomical atlases allows experts to quantitatively compare multimodal networks on different scales. Additionally, 3D visualizations have been optimized to accommodate domain experts’ needs for publishable network figures. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning functional neuroanatomy in mice.
On Quantifying Local Geometric Structures of Fiber Tracts
MICCAI invited talk
In diffusion MRI, fiber tracts, represented by densely distributed 3D curves, can be estimated from diffusion weighted images using tractography. The spatial geometric structure of white matter fiber tracts is known to be complex in human brain, but it carries intrinsic information of human brain. In this paper, inspired by studies of liquid crystals, we propose tract-based director field analysis (tDFA) with total six rotational invariant scalar indices to quantify local geometric structures of fiber tracts. The contributions of tDFA include: 1) We propose orientational order (OO) and orientational dispersion (OD) indices to quantify the degree of alignment and dispersion of fiber tracts; 2) We define the local orthogonal frame for a set of unoriented curves, which is proved to be a generalization of the Frenet frame defined for a single oriented curve; 3) With the local orthogonal frame, we propose splay, bend, and twist indices to quantify three types of orientational distortion of local fiber tracts, and a total distortion index to describe distortions of all three types. The proposed tDFA for fiber tracts is a generalization of the voxel-based DFA (vDFA) which was recently proposed for a spherical function field (i.e., an ODF field). To our knowledge, this is the first work to \emph{quantify} orientational distortion (splay, bend, twist, and total distortion) of fiber tracts. Experiments show that the proposed scalar indices are useful descriptors of local geometric structures to visualize and analyze fiber tracts. The codes and a tutorial on tDFA will be released.
Progressive and Efficient Multi-Resolution Representations for Brain Tractograms
Short paper
Current tractography methods generate tractograms composed of millions of 3D polylines, called fibers, making visualization and interpretation extremely challenging, thus complexifying the use of this technique in a clinical environment. We propose to progressively simplify tractograms by grouping similar fibers into generalized cylinders. This produces a fine-grained multi-resolution model that provides a progressive and real-time navigation through different levels of detail. This model preserves the overall structure of the tractogram and can be adapted to different measures of similarity. We also provide an efficient implementation of the method based on a Delaunay tetrahedralization. We illustrate our method using the Human Connectome Project dataset.
Visual Analysis of Evolution of EEG Coherence Networks employing Temporal Multidimensional Scaling
Short Paper
The community structure of networks plays an important role in their analysis. It represents a high-level organization of objects within a network. However, in many application domains, the relationship between objects in a network changes over time, resulting in the change of community structure (the partition of a network), their attributes (the composition of a community and the values of relationships between communities), or both. Previous animation or timeline-based representations either visualize the change of attributes of networks or the community structure. There is no single method that can optimally show graphs that change in both structure and attributes. In this paper, we propose a method for the case of dynamic EEG coherence networks to assist users in exploring the dynamic changes in both their community structure and their attributes. The method uses an initial timeline representation which was designed to provide an overview of changes in community structure. In addition, we order communities and assign colors to them based on their relationships by adapting the existing Temporal Multidimensional Scaling (TMDS) method. Users can identify evolution patterns of dynamic networks from this visualization.
Introducing CNN-Based Mouse Grim Scale Analysis for Fully Automated Image-Based Assessment of Distress in Laboratory Mice
Short paper
Thermal infrared imaging is an emerging imaging modality allowing capturing heat radiation not detectable in the visible spectrum. In recent years, numerous applications of thermal infrared imaging for the processing of face images have been published. Many of these approaches only allow minimal head movement due to the lack of sufficiently robust face tracking and interpretation algorithms for thermal infrared images. To address this issue, we present a suite of interconnected algorithms for a number of typical facial image processing tasks – face detection, head pose estimation, detection and tracking of facial landmarks and facial expression analysis. The modules can be used independently or in combination with each other. For combined use and as a demonstration of the versatility of our solution, we present a multiprocess solution based on a networking middleware that allows using all proposed algorithms to perform real-time face tracking and emotion recognition in thermal infrared images. The full code is made freely available on gitub under GNU license to allow incorporating our solutions into own projects.