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The role of HG in the analysis of temporal iteration and interaural correlation
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Anterior/Posterior Competitive Deactivation/Activation Dichotomy in the Human Hippocampus as Revealed by a 3D Navigation Task
Anterior/posterior long axis specialization is thought to underlie the organization of the hippocampus. However it remains
unclear whether antagonistic mechanisms differentially modulate processing of spatial information within the
hippocampus. We used fMRI and a virtual reality 3D paradigm to study encoding and retrieval of spatial memory during
active visuospatial navigation, requiring positional encoding and retrieval of object landmarks during the path. Both
encoding and retrieval elicited BOLD activation of the posterior most portion of hippocampus, while concurrent
deactivations (recently shown to reflect decreases in neural responses) were found in the most anterior regions. Encoding
elicited stronger activity in the posterior right than the left hippocampus. The former structure also showed significantly
stronger activity for allocentric vs. egocentric processing during retrieval. The anterior vs. posterior pattern mimics, from a
functional point, although at much distinct temporal scales, the previous anatomical findings in London taxi drivers,
whereby posterior enlargement was found at the cost of an anterior decrease, and the mirror symmetric findings observed
in blind people, in whom the right anterior hippocampus was found to be larger, at the cost of a smaller posterior
hippocampus, as compared with sighted people. In sum, we found a functional dichotomy whereby the anterior/posterior
hippocampus shows antagonistic processing patterns for spatial encoding and retrieval of 3D spatial information. To our
knowledge, this is the first study reporting such a dynamical pattern in a functional study, which suggests that differential
modulation of neural responses within the human hippocampus reflects distinct roles in spatial memory processing
3D Architectural Analysis of Neurons, Astrocytes, Vasculature & Nuclei in the Motor and Somatosensory Murine Cortical Columns
Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, the sheer density of filamentous processes in the neuropil significantly complicates analysis due to the difficulty of separating individual cells in a highly interconnected network of tightly woven cellular arbors. In order to solve these problems, a variety of methodologies were developed and validated for improved analysis of cortical anatomy. An enhanced immunofluorescent staining and imaging protocol was utilized to precisely locate specific functional regions within brain slices at high magnification and collect four-channel, complete cortical columns. A powerful deconvolution routine was established which collected depth variant PSFs using an optical phantom for image restoration. Fractional volume analysis (FVA) was used to provide preliminary data of the proportions of each stained component in order to statistically characterize the variability within and between the functional regions in a depth-dependent and depth-independent manner. Finally, using machine learning techniques, a supervised learning model was developed that could automatically classify neuronal and astrocytic nuclei within the large cortical column datasets based on perinuclear fluorescence. These annotated nuclei were then used as seed points within their corresponding fluorescent channel for cell individualization in a highly interconnected network. For astrocytes, this technique provides the first method for characterization of complex morphology in an automated fashion over large areas without laborious dye filling or manual tracing
Towards Advanced Interactive Visualization for Virtual Atlases
Under embargo until: 2020-07-24An atlas is generally defined as a bound collection of tables, charts or illustrations describing a phenomenon. In an anatomical atlas for example, a collection of representative illustrations and text describes anatomy for the purpose of communicating anatomical knowledge. The atlas serves as reference frame for comparing and integrating data from different sources by spatially or semantically relating collections of drawings, imaging data, and/or text. In the field of medical image processing, atlas information is often constructed from a collection of regions of interest, which are based on medical images that are annotated by domain experts. Such an atlas may be employed, for example, for automatic segmentation of medical imaging data. The combination of interactive visualization techniques with atlas information opens up new possibilities for content creation, curation, and navigation in virtual atlases. With interactive visualization of atlas information, students are able to inspect and explore anatomical atlases in ways that were not possible with the traditional method of presenting anatomical atlases in book format, such as viewing the illustrations from other viewpoints. With advanced interaction techniques, it becomes possible to query the data that forms the basis for the atlas, thus empowering researchers to access a wealth of information in new ways. So far, atlas-based visualization has been employed mainly for medical education, as well as biological research. In this survey, we provide an overview of current digital biomedical atlas tasks and applications and summarize relevant visualization techniques. We discuss recent approaches for providing next-generation visual interfaces to navigate atlas data that go beyond common text-based search and hierarchical lists. Finally, we reflect on open challenges and opportunities for the next steps in interactive atlas visualization.acceptedVersio
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