Literature DB >> 15125002

Segmentation of nodules on chest computed tomography for growth assessment.

William Mullally1, Margrit Betke, Jingbin Wang, Jane P Ko.   

Abstract

Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist.

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Year:  2004        PMID: 15125002     DOI: 10.1118/1.1656593

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  11 in total

1.  Shape "break-and-repair" strategy and its application to automated medical image segmentation.

Authors:  Jiantao Pu; David S Paik; Xin Meng; Justus E Roos; Geoffrey D Rubin
Journal:  IEEE Trans Vis Comput Graph       Date:  2011-01       Impact factor: 4.579

2.  Pulmonary fissure segmentation on CT.

Authors:  Jingbin Wang; Margrit Betke; Jane P Ko
Journal:  Med Image Anal       Date:  2006-06-27       Impact factor: 8.545

3.  Insertion of virtual pulmonary nodules in CT data of the chest: development of a software tool.

Authors:  Hoen-oh Shin; Matthias Blietz; Bernd Frericks; Stefan Baus; Dagmar Savellano; Michael Galanski
Journal:  Eur Radiol       Date:  2006-07-04       Impact factor: 5.315

4.  Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners.

Authors:  Marco Das; Julia Ley-Zaporozhan; H A Gietema; Andre Czech; Georg Mühlenbruch; Andreas H Mahnken; Markus Katoh; Annemarie Bakai; Marcos Salganicoff; Stefan Diederich; Mathias Prokop; Hans-Ulrich Kauczor; Rolf W Günther; Joachim E Wildberger
Journal:  Eur Radiol       Date:  2007-01-06       Impact factor: 5.315

5.  Computer-aided diagnosis systems for lung cancer: challenges and methodologies.

Authors:  Ayman El-Baz; Garth M Beache; Georgy Gimel'farb; Kenji Suzuki; Kazunori Okada; Ahmed Elnakib; Ahmed Soliman; Behnoush Abdollahi
Journal:  Int J Biomed Imaging       Date:  2013-01-29

6.  Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

Authors:  Matthew C Hancock; Jerry F Magnan
Journal:  J Med Imaging (Bellingham)       Date:  2016-12-08

7.  Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT.

Authors:  Wei Guo; Qiang Li
Journal:  Med Phys       Date:  2014-09       Impact factor: 4.071

8.  Computerized segmentation of pulmonary nodules depicted in CT examinations using freehand sketches.

Authors:  Yongqian Qiang; Qiuping Wang; Guiping Xu; Hongxia Ma; Lei Deng; Lei Zhang; Jiantao Pu; Youmin Guo
Journal:  Med Phys       Date:  2014-04       Impact factor: 4.071

9.  A Computational geometry approach to automated pulmonary fissure segmentation in CT examinations.

Authors:  Jiantao Pu; Joseph K Leader; Bin Zheng; Friedrich Knollmann; Carl Fuhrman; Frank C Sciurba; David Gur
Journal:  IEEE Trans Med Imaging       Date:  2008-12-09       Impact factor: 10.048

10.  Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field.

Authors:  Yongqiang Tan; Lawrence H Schwartz; Binsheng Zhao
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

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