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      Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease

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      Medical Image Analysis
      Elsevier BV

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          Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

          Summary Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).
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            Focal Loss for Dense Object Detection

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              Comparing images using the Hausdorff distance

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                Author and article information

                Contributors
                Journal
                Medical Image Analysis
                Medical Image Analysis
                Elsevier BV
                13618415
                October 2020
                October 2020
                : 65
                : 101791
                Article
                10.1016/j.media.2020.101791
                32712525
                db7d9684-93e2-4f93-8eca-8a1f89db77d0
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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