Titlebook: Image Analysis for Moving Organ, Breast, and Thoracic Images; Third International Danail Stoyanov,Zeike Taylor,Catarina Veiga Conference p

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Resection-Based Demons Regularization for Breast Tumor Bed Propagationver the real deformations when the structure of interest is missing. In this work, we propose an empirical, greedy regularization term which promotes the tumor contraction. The proposed method is simple but very effective. It is based on a priori medical knowledge about the scar localization to prom

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有角

Super Resolution of Cardiac Cine MRI Sequences Using Deep Learningt of sparse 2D images instead of 3D volumes, taken at landmark points of the ECG to cover the whole heartbeat. A stack of short axis images and a small number of long axis views are generally acquired. Efforts have been made to accelerate acquisitions at the acquisition stage as well as at post-proc

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Automated CNN-Based Reconstruction of Short-Axis Cardiac MR Sequence from Real-Time Image Data who are too ill or otherwise incapable of consistent breath holds, real-time MR sequences are the preferred means of acquiring cardiac images, but suffer from inferior image quality compared to standard short-axis sequences and lack cardiac ECG gating. To construct a sequence from real-time images

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micronutrients

ENACT

Large-Scale Mammography CAD with Deformable Conv-Nets. Among them, region-based fully convolutional networks (R-FCN) and deformable convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for speed and low consumption of memory, which is crucial for processing the high resolutions of to . used by radiologists. Deformable convolution

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neutrophils

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