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      msemalign: a pipeline for serial section multibeam scanning electron microscopy volume alignment

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          Abstract

          Serial section multibeam scanning electron microscopy (ssmSEM) is currently among the fastest technologies available for acquiring 3D anatomical data spanning relatively large neural tissue volumes, on the order of 1 mm 3 or larger, at a resolution sufficient to resolve the fine detail of neuronal morphologies and synapses. These petabyte-scale volumes can be analyzed to create connectomes, datasets that contain detailed anatomical information including synaptic connectivity, neuronal morphologies and distributions of cellular organelles. The mSEM acquisition process creates hundreds of millions of individual image tiles for a single cubic-millimeter-sized dataset and these tiles must be aligned to create 3D volumes. Here we introduce msemalign, an alignment pipeline that strives for scalability and design simplicity. The pipeline can align petabyte-scale datasets such that they contain smooth transitions as the dataset is navigated in all directions, but critically that does so in a fashion that minimizes the overall magnitude of section distortions relative to the originally acquired micrographs.

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

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            Adam: A Method for Stochastic Optimization

            We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015
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              Distinctive Image Features from Scale-Invariant Keypoints

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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/136568/overviewRole: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2510331/overviewRole: Role: Role:
                URI : https://loop.frontiersin.org/people/2265818/overviewRole: Role: Role: Role: Role: Role:
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                12 December 2023
                2023
                : 17
                : 1281098
                Affiliations
                Max Planck Institute for Neurobiology of Behavior—caesar , Bonn, Germany
                Author notes

                Edited by: Xueying Wang, Jura Bio, Inc., United States

                Reviewed by: Michal Januszewski, Google Zurich, Switzerland; Yuelong Wu, Harvard University, United States

                *Correspondence: Kevin L. Briggman, kevin.briggman@ 123456mpinb.mpg.de
                Article
                10.3389/fnins.2023.1281098
                10749929
                38148945
                b2670c20-8d45-4b29-82a8-1adad3f2361d
                Copyright © 2023 Watkins, Jelli and Briggman.

                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
                : 21 August 2023
                : 23 November 2023
                Page count
                Figures: 10, Tables: 1, Equations: 0, References: 27, Pages: 19, Words: 13149
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study received funding from the Max Planck Society.
                Categories
                Neuroscience
                Original Research
                Custom metadata
                Brain Imaging Methods

                Neurosciences
                multibeam,alignment,serial section electron microscopy,pipeline,sem
                Neurosciences
                multibeam, alignment, serial section electron microscopy, pipeline, sem

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