papertrans 发表于 2025-3-21 17:01:25
书目名称Biomedical Image Registration影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0188051<br><br> <br><br>书目名称Biomedical Image Registration读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0188051<br><br> <br><br>BOLT 发表于 2025-3-21 21:08:17
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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 multieuphoria 发表于 2025-3-22 14:24:48
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 multiresolutionFierce 发表于 2025-3-22 19:25:05
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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 evaVERT 发表于 2025-3-23 05:16:41
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