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Titlebook: Abdominal Imaging. Computational and Clinical Applications; 5th International Wo Hiroyuki Yoshida,Simon Warfield,Michael W. Vannier Confere

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期刊全称Abdominal Imaging. Computational and Clinical Applications
期刊简称5th International Wo
影响因子2023Hiroyuki Yoshida,Simon Warfield,Michael W. Vannier
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Abdominal Imaging. Computational and Clinical Applications; 5th International Wo Hiroyuki Yoshida,Simon Warfield,Michael W. Vannier Confere
影响因子This book constitutes the refereed proceedings of the 5th International Workshop CCAA 2013, held in conjunction with MICCAI 2013, in Nagoya, Japan, in September 2013. The book includes 32 papers which were carefully reviewed and selected from 38 submissions. The topics covered are abdominal atlases, shape analysis and morphology in abdominal structures and organs, detection of anatomical and functional landmarks, dynamic, functional, physiologic, and anatomical abdominal imaging, registration methods for abdominal intra- and inter-patient variability, augmented reality techniques for intervention, clinical applications in radio-frequency ablation, open surgery, and minimally invasive surgery.
Pindex Conference proceedings 2013
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Stefan Henningsson,Gustav Normark Toppenberge metric requires recalculation of the self similarity during registration as this inherently changes during image deformation. We propose a self similarity registration method based on the Hessian (HE) that efficiently deals with the recalculation issue. The representation of the local self-similar
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https://doi.org/10.1007/978-3-030-38610-8pecially in sub-optimally prepared patients. This paper introduces a prone and supine registration method that aims to overcome the difficulties posed by differences in luminal distension and bowel cleansing. The endoluminal surface is iteratively deformed using thin plate spline interpolation in or
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Christian Bunse,Hans-Gerhard Grosslonography (CTC). We developed a sinogram-based super-resolution (SR) method to enhance the images of lesion candidates detected by CADe. First, CADe is used to detect lesion candidates at high sensitivity from conventional CTC images. Next, the signal patterns of the lesion candidates are enhanced
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