By Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, Miguel Angel González Ballester, Klaus Drechsler (eds.)
This publication constitutes revised chosen papers from the overseas Workshop on scientific Image-Based strategies, CLIP 2013, held at the side of MICCAI 2012 in Nagoya, Japan, in September 2013. the nineteen papers provided during this quantity have been conscientiously reviewed and chosen from 26 submissions. The workshop used to be a efficient and fascinating discussion board for the dialogue and dissemination of clinically established, state of the art tools for image-based making plans, tracking and assessment of clinical procedures.
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Extra resources for Clinical Image-Based Procedures. Translational Research in Medical Imaging: Second International Workshop, CLIP 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Revised Selected Papers
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Then, a virtual ray from detector source to the selected pixel on the ﬂuoroscopy image is deﬁned. The intersecting bronchial tree segmentation voxels give the current 3D position of the bronchoscope tip. Given this position, we ﬁnd the corresponding bronchus and the path to the trachea using the labelled skeleton of the bronchial tree (see Fig. 1(c)). This step is illustrated in Fig. 5. 4 Results All preoperative and intraoperative components were integrated into one system for 3D bronchoscopy navigation, which we evaluated partly on clinical patient data and partly on phantom images.
Illustrative example of MRF-based segmentation at the level of the lateral ventricles. (a) A 10-mm minimum intensity projection (minIP) transverse slab from a raw SWI dataset (b) MRF segmentation. (c) Comparison to scale-space vesselness filtering. (red boxes) ROIs of the deep venous system and left/right sub-cortical veins used for validation of Sect. 2. (Color figure online) 44 S. Bériault et al. Fig. 2. Automatic segmentation of the surface vasculature. (a) A raw SWI slice at native resolution.