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Download Abdominal Imaging. Computational and Clinical Applications: by Holger R. Roth, Thomas E. Hampshire, Jamie R. McClelland, PDF

By Holger R. Roth, Thomas E. Hampshire, Jamie R. McClelland, Mingxing Hu, Darren J. Boone (auth.), Hiroyuki Yoshida, Georgios Sakas, Marius George Linguraru (eds.)

This publication constitutes the completely refereed post-conference complaints of the 3rd overseas Workshop on Computational and medical purposes in stomach Imaging, held together with MICCAI 2011, in Toronto, Canada, on September 18, 2011. The 33 revised complete papers provided have been conscientiously reviewed and chosen from forty submissions. The papers are geared up in topical sections on digital colonoscopy and CAD, stomach intervention, and computational stomach anatomy.

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Additional resources for Abdominal Imaging. Computational and Clinical Applications: Third International Workshop, Held in Conjunction with MICCAI 2011, Toronto, ON, Canada, September 18, 2011, Revised Selected Papers

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Brackets indicate 95% confidence intervals. 000001 Table 2 shows the per-lesion detection accuracy for advanced lesions with polypoid morphology (n=232). Again, the RF classifier outperformed the SVM classifier with statistically significant improvement. Table 2. Per-lesion detection accuracy between the RF and SVM classifiers for advanced polypoid lesions. Brackets indicate 95% confidence intervals. 000001 Table 3 shows the per-lesion detection performance for advanced flat lesions (n=30). In the detection of moderate-size flat lesions (6 – 9 mm), the RF classifier outperformed the SVM classifier with statistically significant improvement.

The statistical significance was computed using ROCKIT. 1 0 0 2 4 6 8 10 12 14 16 18 20 FP/scan Fig. 6. FROC curves of the performance of the CAD system before and after cleansing Fig. 7. (A) CT image depicting poorly tagged heterogeneous stool. (B) Erroneous subtraction. 4 Discussion We presented a method to automatically subtract tagged stool from cathartic-free CT data to aid in the detection of colorectal cancer. The stool subtraction algorithm was particularly designed to address the challenges of local variability in tagging, pseudoenhancement of submerged colon tissue, and the heterogeneity of stool enhancement.

Abdominal Imaging 2011, LNCS 7029, pp. 18–26, 2012. © Springer-Verlag Berlin Heidelberg 2012 Computer-Aided Polyp Detection for Laxative-Free CT Colonography 19 to poorly and heterogeneously tagged stool, thin stool close to the colon walls, pseudoenhancement of colon tissue, and partial volume effect. In recent years, several studies have been developed for image-based colon cleansing, which perform well on fully homogeneously tagged stool [4,5,8]. Fewer publications addressed the cleansing of heterogeneously tagged stool.

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