Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks

Microscopic analysis of histological sections is considered the "gold standard" to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does not scale with high throughput imaging. We present an automatic approach for parcellating histological sections at 2um resolution. It is based on a convolutional neural network that combines topological information from probabilistic atlases with the texture features learned from high-resolution cell-body stained images. The model is applied to visual areas and trained on a sparse set of partial annotations. We show how predictions are transferable to new brains and spatially consistent across sections.Comment: Accepted for oral presentation at International Symposium of Biomedical Imaging (ISBI) 201

Diattenuation of Brain Tissue and its Impact on 3D Polarized Light Imaging

3D-Polarized Light Imaging (3D-PLI) reconstructs nerve fibers in histological brain sections by measuring their birefringence. This study investigates another effect caused by the optical anisotropy of brain tissue - diattenuation. Based on numerical and experimental studies and a complete analytical description of the optical system, the diattenuation was determined to be below 4 % in rat brain tissue. It was demonstrated that the diattenuation effect has negligible impact on the fiber orientations derived by 3D-PLI. The diattenuation signal, however, was found to highlight different anatomical structures that cannot be distinguished with current imaging techniques, which makes Diattenuation Imaging a promising extension to 3D-PLI.Comment: 32 pages, 15 figure

A Jones matrix formalism for simulating three-dimensional polarized light imaging of brain tissue

The neuroimaging technique three-dimensional polarized light imaging (3D-PLI) provides a high-resolution reconstruction of nerve fibres in human post-mortem brains. The orientations of the fibres are derived from birefringence measurements of histological brain sections assuming that the nerve fibres - consisting of an axon and a surrounding myelin sheath - are uniaxial birefringent and that the measured optic axis is oriented in direction of the nerve fibres (macroscopic model). Although experimental studies support this assumption, the molecular structure of the myelin sheath suggests that the birefringence of a nerve fibre can be described more precisely by multiple optic axes oriented radially around the fibre axis (microscopic model). In this paper, we compare the use of the macroscopic and the microscopic model for simulating 3D-PLI by means of the Jones matrix formalism. The simulations show that the macroscopic model ensures a reliable estimation of the fibre orientations as long as the polarimeter does not resolve structures smaller than the diameter of single fibres. In the case of fibre bundles, polarimeters with even higher resolutions can be used without losing reliability. When taking the myelin density into account, the derived fibre orientations are considerably improved.Comment: 20 pages, 8 figure

Передача данных сетью БПЛА вдоль линейного объекта на противололожных курсах

The process of data transmission by a network of unmanned aerial vehicles (UAVs) based on DPMR (Digital Private Mobile Radio) along an extended linear object is considered. A method of forming a network with the help of two UAVs moving in opposite directions with equal intervals between the devices within each of them is shown

Проблема утилизации и вторичной переработки пластиковых бутылок

Происходящие глобальные изменения преобразовывают обычную сырьевую экономику в высокотехнологичную, позволяющую рационально использовать имеющиеся ресурсы и при этом не загрязнять окружающую нас среду. Переработка ПЭТ-бутылок позволит решить проблему утилизации пластикового мусора и может стать прибыльным бизнесом. Результаты исследования показали, что сырье, полученное в процессе переработки пластиковых бутылок, может быть использовано для изготовления востребованной продукции.The ongoing global changes transform the conventional raw material economy into a high-tech one, allowing rational use of available resources and at the same time to not polluting the environment around us. Recycling of PET bottles will solve the problem of recycling plastic trash and can become a profitable business. The results of the research showed that secondary raw material, obtained during the processing of plastic bottles can be used for the production of the demanded products

Contour Proposal Networks for Biomedical Instance Segmentation

We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incorporate state of the art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in instance segmentation accuracy, and present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework are closed object contours, it is applicable to a wide range of detection problems also outside the biomedical domain. An implementation of the model architecture in PyTorch is freely available

3D Polarized Light Imaging Portrayed: Visualization of Fiber Architecture Derived from 3D-PLI

3D polarized light imaging (3D-PLI) is a neuroimaging technique that has recently opened up new avenues to study the complex architecture of nerve fibers in postmortem brains at microscopic scales. In a specific voxel-based analysis, each voxel is assigned a single 3D fiber orientation vector. This leads to comprehensive 3D vector fields. In order to inspect and analyze such high-resolution fiber orientation vector field, also in combination with complementary microscopy measurements, appropriate visualization techniques are essential to overcome several challenges, such as the massive data sizes, the large amount of both unique and redundant information at different scales, or the occlusion issues of inner structures by outer layers. Here, we introduce a comprehensive software tool that is able to visualize all information of a typical 3D-PLI dataset in an adequate and sophisticated manner. This includes the visualization of (i) anatomic structural and fiber architectonic data in one representation, (ii) a large-scale fiber orientation vector field, and (iii) a clustered version of the field. Alignment of a 3D-PLI dataset to an appropriate brain atlas provides expert-based delineation, segmentation, and, ultimately, visualization of selected anatomical structures. By means of these techniques, a detailed analysis of the complex fiber architecture in 3D is feasible

Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections

Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as measured from histological tissue sections. Recent work provided the first automatic segmentations of cytoarchitectonic areas in the visual system using Convolutional Neural Networks. We aim to extend this approach to become applicable to a wider range of brain areas, envisioning a solution for mapping the complete human brain. Inspired by recent success in image classification, we propose a contrastive learning objective for encoding microscopic image patches into robust microstructural features, which are efficient for cytoarchitectonic area classification. We show that a model pre-trained using this learning task outperforms a model trained from scratch, as well as a model pre-trained on a recently proposed auxiliary task. We perform cluster analysis in the feature space to show that the learned representations form anatomically meaningful groups.Comment: Accepted to ISBI 202

Разработка бесконтактного кардиографа

Целью данной работы является разработка бесконтактного электрокардиографа и анализ его свойств. В процессе изучения были рассмотрены основные принципы электрокардиографии и проведен ряд экспериментов по исследованию электрокардиографических свойств. В результате исследования были изучены вопросы и проблемы связанные с реализацией разработки бесконтактного электрокардиографа, также были получены оценки его характеристики.The aim of this work is to develop a non-contact electrocardiograph and analysis of its properties. During the study, the basic principles of electrocardiography were considered and a number of experiments were conducted to study the electrocardiographic properties. As a result of the study, questions and problems related to the implementation of the development of a non-contact electrocardiograph were studied, and estimates of its characteristics were also obtained