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Titlebook: Biomedical Image Registration; 9th International Wo ?iga ?piclin,Jamie McClelland,Orcun Goksel Conference proceedings 2020 Springer Nature

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期刊全稱Biomedical Image Registration
期刊簡稱9th International Wo
影響因子2023?iga ?piclin,Jamie McClelland,Orcun Goksel
視頻videohttp://file.papertrans.cn/189/188051/188051.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Biomedical Image Registration; 9th International Wo ?iga ?piclin,Jamie McClelland,Orcun Goksel Conference proceedings 2020 Springer Nature
影響因子.This book constitutes the refereed proceedings of the 9th International Workshop on Biomedical Image Registration, WBIR 2020, which was supposed to be held in Portoro?, Slovenia, in June 2020. The conference was postponed until December 2020 due to the COVID-19 pandemic...The 16 full and poster papers included in this volume were carefully reviewed and selected from 22 submitted papers. The papers are organized in the following topical sections: Registration initialization and acceleration, interventional registration, landmark based registration, multi-channel registration, and sliding motion..
Pindex Conference proceedings 2020
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Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapyder or rectum, that often yield extreme deformations. In this work we analyze the impact of so-called structure guidance for learning based registration when additional segmentation information is provided to a neural network. We present a novel weakly supervised deep learning based method for multi
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Multilevel 2D-3D Intensity-Based Image?Registrationration is the use of multilevel strategies to avoid local optima and to speed-up runtime. However, due to the different dimensionalities of the 2D fixed and 3D moving image, the setup of multilevel strategies is not straightforward..In this work, we propose an intensity-driven 2D-3D multiresolution
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Reinforced Redetection of Landmark in Pre- and Post-operative Brain Scan Using Anatomical Guidance flly due to tumor resection and tissue displacement. Classical image registration techniques oftentimes fail in vicinity of the tumor, where the enclosing structures are massively altered from one scan to another. Still, locations nearby the tumor or the resection cavity are the most relevant for eva
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