Literature DB >> 10739316

Automatic segmentation of the colon for virtual colonoscopy.

C L Wyatt1, Y Ge, D J Vining.   

Abstract

Virtual colonoscopy is a minimally invasive technique that enables early detection of colorectal polyps and cancer. Normally, a patient's bowel is prepared with colonic lavage and gas insufflation prior to computed tomography scanning. An important step for 3D analysis of the image volume is segmentation of the colon. The high-contrast gas/tissue interface that exists in the colon lumen makes segmentation of the majority of the colon relatively easy; however, two factors inhibit automatic segmentation of the entire colon. First, the colon is not the only gas-filled organ in the data volume: lungs, small bowel, and stomach also meet this criterion. User-defined seed points placed in the colon lumen have previously been required to spatially isolate the colon. Second, portions of the colon lumen may be obstructed by peristalsis, large masses, and/or residual feces. These complicating factors require increased user interaction during the segmentation process to isolate additional colonic segments. To automate the segmentation of the colon, we have developed a method to locate seed points and segment the gas-filled lumen sections without user supervision. We have also developed an automated approach to improve lumen segmentation by digitally removing residual contrast-enhanced fluid. Experimental results with 20 patient volumes show that our method is accurate and reliable.

Entities:  

Mesh:

Year:  2000        PMID: 10739316     DOI: 10.1016/s0895-6111(99)00039-7

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  4 in total

1.  Does the amount of tagged stool and fluid significantly affect the radiation exposure in low-dose CT colonography performed with an automatic exposure control?

Authors:  Hyun Kyong Lim; Kyoung Ho Lee; So Yeon Kim; Kil Joong Kim; Bohyoung Kim; Hyunna Lee; Seong Ho Park; Jeffrey H Yanof; Seung-Sik Hwang; Young Hoon Kim
Journal:  Eur Radiol       Date:  2010-08-11       Impact factor: 5.315

2.  Automatic colon segmentation with dual scan CT colonography.

Authors:  Hong Li; Peter Santago
Journal:  J Digit Imaging       Date:  2005-03       Impact factor: 4.056

Review 3.  Computer-aided detection for virtual colonoscopy.

Authors:  James J Perumpillichira; Hiroyuki Yoshida; Dushyant V Sahani
Journal:  Cancer Imaging       Date:  2005-08-23       Impact factor: 3.909

4.  Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy.

Authors:  Joonmyeong Choi; Keewon Shin; Jinhoon Jung; Hyun-Jin Bae; Do Hoon Kim; Jeong-Sik Byeon; Namku Kim
Journal:  Clin Endosc       Date:  2020-03-30
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.