Download Abdominal Imaging. Computational and Clinical Applications: by Thomas E. Hampshire, Holger R. Roth, Darren J. Boone, Greg PDF

By Thomas E. Hampshire, Holger R. Roth, Darren J. Boone, Greg Slabaugh, Steve Halligan (auth.), Hiroyuki Yoshida, David Hawkes, Michael W. Vannier (eds.)

ISBN-10: 3642336116

ISBN-13: 9783642336119

This booklet constitutes the refereed lawsuits of the foreign Workshop CCAAI 2012, held in great, France, in October 2012. The booklet comprises 31 papers which have been rigorously reviewed and chosen from 37 submissions. all the accredited papers have been revised by means of incorporating of the reviewers’ reviews and re-submitted via the authors to be integrated during this lawsuits quantity. The papers are geared up into topical sections on colon and different gastrointestinal tract; and liver, kidney, and different organs.

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Additional info for Abdominal Imaging. Computational and Clinical Applications: 4th International Workshop, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012. Proceedings

Example text

3 Results There were 11 lesions ≥10 mm and 16 lesions 6 – 9 mm in largest diameter in the 23 patients. These included 4 cancers, 20 adenomas, and 3 hyperplastic lesions. J. H. Kim, and H. Yoshida Fig. 5 shows the effect of the thickness of target region on the per-lesion sensitivity of the CAD scheme. For lesions ≥10 mm in size (solid line), the detection sensitivity was 100% at all thicknesses except at d = 2 mm. For lesions 6 – 9 mm in size (dotted line), the detection sensitivity peaked out at d = 3 mm (94%), and then it declined rapidly as the wall thickness increased.

Find the shortest loop min{pl } among all the vertices on G. ¯ 5. Cut M along min{pl } and fill the 2 holes appeared, get M. 6. If M¯ is not genus 0, goto step 1. noise. As our method find tiny handles based on surface topology and remove them one by one, the final surface is guaranteed to be genus 0. The pipeline is like the following: The idea of efficient topological de-noise algorithm is like following: we can compute the shortest loop goes though vertex v for all the vertices in mesh M, then find the shortest one among them, it must be the shortest handle loop in M.

4. Left: virtual endoluminal view of an obscure object (indicated by arrow) on fold. Middle: axial CTC image of the object. Right: high values of the dual-energy index in the axial image indicate that the object is a partial-volume tagging artifact. 6 Evaluation The detection accuracy of CAD was evaluated at wall-thickness values of 2 mm, 3 mm, 4 mm, 5 mm, and 6 mm, by use of a leave-one-patient-out evaluation. A colonoscopy-confirmed lesion was considered to be detected correctly by CAD, if the center of a lesion candidate detected by CAD was within the radius of the center of a true lesion in CTC data.

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