Ultrahigh resolution 3D cytoarchitectonic map of the LGB (lam 1-6, CGL, Metathalamus) created by a Deep-Learning assisted workflow

This dataset contains automatically created cytoarchitectonic maps of the six distinct layers (LGB-lam1-6) of the lateral geniculate body – LGB (CGL, Metathalamus) in the BigBrain (LGB is equivalent to CGL and can be used as synonyms). Mappings were created using Deep Convolutional Neural networks trained on delineations on every 30th section manually delineated on coronal histological sections of 1 micron resolution. Resulting mappings are available on every section. Maps were transformed to the 3D reconstructed BigBrain space. Individual sections were used to assemble a 3D volume of the area, low quality results were replaced by interpolations between nearest neighboring sections. The volume was then smoothed using an 5³ median filter and largest connected components were identified to remove false positive results. The dataset consists of a HDF5 file containing the volume in RAS dimension ordering (20 micron isotropic resolution, dataset “volume”) and an affine transformation matrix (dataset “affine”). An additional dataset “interpolation_info” contains an integer vector for each section which indicates if a section was interpolated due to low quality results (value 2) or not (value 1)

Reference delineations of the LGB (lam 1-6, CGL, Metathalamus) in individual sections of the BigBrain

This dataset contains cytoarchitectonic maps of the six distinct layers (lam 1-6) of the lateral geniculate body – LGB (CGL, Metathalamus) in the BigBrain (LGB is equivalent to CGL and can be used as synonyms). The mappings were created using cytoarchitectonic criteria applied on digitized histological sections of 1 μm resolution cut in coronal plane. Mappings are available on 15 sections of the diencephalon of the BigBrain and have been transformed to the 3D reconstructed BigBrain space. For this brain area, a highly detailed 3D map has been computed based on automatic delineations on every histological section from a novel Deep Learning algorithm. This ultrahigh resolution 3D cytoarchitectonic map of the LGB can be explored in the HBP interactive Atlas Viewer. This dataset can be accessed here: [Ultrahigh resolution 3D cytoarchitectonic map of LGB (CGL, Metathalamus) created by a Deep-Learning assisted workflow.](https://search.kg.ebrains.eu/instances/d0c36f4a-91a8-4885-880d-f2896f5c54cf

High-resolution fiber tract reconstruction in the human brain by means of the three-dimensional polarized light imaging (3D-PLI)

Functional interactions between different brain regions require connecting fiber tracts, the structural basis of the human connectome. To assemble a comprehensive structural understanding of neural network elements from the microscopic to the macroscopic dimensions, a multimodal and multiscale approach has to be envisaged. However, the integration of results from complementary neuroimaging techniques poses a particular challenge. In this paper, we describe a steadily evolving neuroimaging technique referred to as three-dimensional polarized light imaging (3D-PLI). It is based on the birefringence of the myelin sheaths surrounding axons, and enables the high-resolution analysis of myelinated axons constituting the fiber tracts. 3D-PLI provides the mapping of spatial fiber architecture in the postmortem human brain at a sub-millimeter resolution, i.e., at the mesoscale. The fundamental data structure gained by 3D-PLI is a comprehensive 3D vector field description of fibers and fiber tract orientations - the basis for subsequent tractography. To demonstrate how 3D-PLI can contribute to unravel and assemble the human connectome, a multiscale approach with the same technology was pursued. Two complementary state-of-the-art polarimeters providing different sampling grids (pixel sizes of 100 and 1.6 μm) were used. To exemplarily highlight the potential of this approach, fiber orientation maps and 3D fiber models were reconstructed in selected regions of the brain (e.g., Corpus callosum, Internal capsule, Pons). The results demonstrate that 3D-PLI is an ideal tool to serve as an interface between the microscopic and macroscopic levels of organization of the human connectome

Automatic identification of gray and white matter components in polarized light imaging

Polarized light imaging (PLI) enables the visualization of fiber tracts with high spatial resolution in microtome sections of postmortem brains. Vectors of the fiber orientation defined by inclination and direction angles can directly be derived from the optical signals employed by PLI analysis. The polarization state of light propagating through a rotating polarimeter is varied in such a way that the detected signal of each spatial unit describes a sinusoidal signal. Noise, light scatter and filter inhomogeneities, however, interfere with the original sinusoidal PLI signals, which in turn have direct impact on the accuracy of subsequent fiber tracking. Recently we showed that the primary sinusoidal signals can effectively be restored after noise and artifact rejection utilizing independent component analysis (ICA). In particular, regions with weak intensities are greatly enhanced after ICA based artifact rejection and signal restoration. Here, we propose a user independent way of identifying the components of interest after decomposition; i.e., components that are related to gray and white matter. Depending on the size of the postmortem brain and the section thickness, the number of independent component maps can easily be in the range of a few ten thousand components for one brain. Therefore, we developed an automatic and, more importantly, user independent way of extracting the signal of interest. The automatic identification of gray and white matter components is based on the evaluation of the statistical properties of the so-called feature vectors of each individual component map, which, in the ideal case, shows a sinusoidal waveform. Our method enables large-scale analysis (i.e., the analysis of thousands of whole brain sections) of nerve fiber orientations in the human brain using polarized light imaging

Foreground Segmentation from Occlusions Using Structure and Motion Recovery

Abstract. The segmentation of foreground objects in camera images is a fundamental step in many computer vision applications. For visual effect creation, the foreground segmentation is required for the integration of virtual objects between scene elements. On the other hand, camera and scene estimation is needed to integrate the objects perspectively correct into the video. In this paper, discontinued feature tracks are used to detect occlusions. If these features reappear after their occlusion, they are connected to the correct previously discontinued trajectory during sequential camera and scene estimation. The combination of optical flow for features in consecutive frames and SIFT matching for the wide baseline feature connection provides accurate and stable feature tracking. The knowledge of occluded parts of a connected feature track is used to feed an efficient segmentation algorithm which crops the foreground image regions automatically. The presented graph cut based segmentation uses a graph contraction technique to minimize the computational expense. The presented application in the integration of virtual objects into video. For this application, the accurate estimation of camera and scene is crucial. The segmentation is used for the automatic occlusion of the integrated objects with foreground scene content. Demonstrations show very realistic results.

BigBrain: An Ultrahigh-Resolution 3D Human Brain Model

Reference brains are indispensable tools in human brain mapping, enabling integration of multimodal data into an anatomically realistic standard space. Available reference brains, however, are restricted to the macroscopic scale and do not provide information on the functionally important microscopic dimension. We created an ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. “BigBrain” is a free, publicly available tool that provides considerable neuroanatomical insight into the human brain, thereby allowing the extraction of microscopic data for modeling and simulation. BigBrain enables testing of hypotheses on optimal path lengths between interconnected cortical regions or on spatial organization of genetic patterning, redefining the traditional neuroanatomy maps such as those of Brodmann and von Economo