Literature DB >> 17945750

Detection of colon wall outer boundary and segmentation of the colon wall based on level set methods.

R Van Uitert1, I Bitter, R M Summers.   

Abstract

Virtual colonoscopy (VC) has become a more prevalent and accepted method to diagnosis colorectal cancer. An essential element to detecting cancerous polyps using VC in conjunction with computer-aided detection is the accurate segmentation of the colon wall. While the inner boundary of the colon wall, the lumen-mucosal boundary, has often been the focus of previous colon segmentation work, detection of the outer wall, the serosal tissue boundary, allows for the segmentation of the colon wall, which is useful in determining both potential polyps, muscular hypertrophy and diverticulitis of the colon. Unfortunately, automatic determination of the outer colon wall position often is difficult due to the low contrast between CT attenuation values of the colon wall and the surrounding fat tissue. We have developed a level set based method to determine from a CT colonography (CTC) scan the location of the colon serosal tissue boundary. After determining this location, the algorithm segments the entire colon wall at subvoxel accurate precision. The algorithm has been validated on several CTC datasets.

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Year:  2006        PMID: 17945750     DOI: 10.1109/IEMBS.2006.260549

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  Volumetric Colon Wall Unfolding Using Harmonic Differentials.

Authors:  Wei Zeng; Joseph Marino; Arie Kaufman; Xianfeng David Gu
Journal:  Comput Graph       Date:  2011-06-01       Impact factor: 1.936

2.  Associations among pericolonic fat, visceral fat, and colorectal polyps on CT colonography.

Authors:  Jiamin Liu; Sanket Pattanaik; Jianhua Yao; Andrew J Dwyer; Perry J Pickhardt; J Richard Choi; Ronald M Summers
Journal:  Obesity (Silver Spring)       Date:  2014-12-31       Impact factor: 5.002

3.  Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.

Authors:  Yesenia Gonzalez; Chenyang Shen; Hyunuk Jung; Dan Nguyen; Steve B Jiang; Kevin Albuquerque; Xun Jia
Journal:  Med Image Anal       Date:  2020-12-16       Impact factor: 8.545

  3 in total

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