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      Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease

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          Abstract

          This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100-micron isotropic resolution to histology sections at 1 × 1 × 1,000-micron resolution with multiple varying stains. We pose registration as a penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting, there is no assumption of symmetry between atlas and target. Cross-modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to “background” or “artifact.” The two concepts are combined within an Expectation-Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally, we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.

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          Most cited references65

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          Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms

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            Mutual-information-based registration of medical images: a survey.

            An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.
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              Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

              The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                11 February 2020
                2020
                : 14
                : 52
                Affiliations
                [1] 1Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD, United States
                [2] 2Center for Imaging Science, Johns Hopkins University , Baltimore, MD, United States
                [3] 3Department of Pathology, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [4] 4Department of Radiology, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [5] 5Department of Neurology, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                Author notes

                Edited by: John Ashburner, University College London, United Kingdom

                Reviewed by: Suyash P. Awate, Indian Institute of Technology Bombay, India; Andrzej Skalski, AGH University of Science and Technology, Poland; Marek Wodzinski, AGH University of Science and Technology, Poland, in collaboration with reviewer AS

                *Correspondence: Daniel Tward dtward@ 123456cis.jhu.edu

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                †Present address: Yusuke Kageyama, Molecular Neuroscience Research Center, Shiga University of Medical Science, Otsu, Japan

                Article
                10.3389/fnins.2020.00052
                7027169
                32038151
                c7c2090a-adf5-4628-ae8f-d29857135c5d
                Copyright © 2020 Tward, Brown, Kageyama, Patel, Hou, Mori, Albert, Troncoso and Miller.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 June 2019
                : 14 January 2020
                Page count
                Figures: 12, Tables: 2, Equations: 14, References: 83, Pages: 18, Words: 11213
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: P41EB015909
                Award ID: P50AG05146
                Award ID: R01EB020062
                Award ID: R01MH105660
                Award ID: R01NS086888
                Award ID: R01NS102670
                Award ID: U19AG033655
                Categories
                Neuroscience
                Technology and Code

                Neurosciences
                neuroimaging,digital pathology,histology,brain mapping,image registration,missing data
                Neurosciences
                neuroimaging, digital pathology, histology, brain mapping, image registration, missing data

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