Literature DB >> 9874837

On segmentation of lung parenchyma in quantitative computed tomography of the lung.

G J Kemerink1, R J Lamers, B J Pellis, H H Kruize, J M van Engelshoven.   

Abstract

Our purpose in this study was to investigate the influence of segmentation threshold and number of erosions on parameters used in quantitative computed tomography (CT) of the lung (erosions are shrink operations on the segmented area). Parameters assessed were mean lung density, area of the segmented lung, two percentiles, and the pixel index, which is the relative area of the histogram below -905 Hounsfield Units (HU). We analyzed images of ten emphysematous and ten nonemphysematous patients, that had been scanned at carina level in inspiration and expiration, using sections of 1, 2, 3, 5, and 10 mm in combination with a standard, a smooth, and an ultrasmooth reconstruction kernel. The lungs were segmented using pixel tracing at thresholds of -200, -400, and -600 HU with 0-4 erosions, followed by histogram analysis. The area of the segmented lungs decreased with 0.9%-3.2% per 100 HU decrease in threshold and with 2.2%-3.1% per erosion, dependent on patient group and respiratory status. Estimated mean lung density changed up to 30% by changing the threshold and the number of erosions. The pixel index and the 10th percentile depended only slightly on threshold and number of erosions, but the 90th percentile showed a strong dependence of up to 40%. It is concluded that the segmentation protocol can have a large impact on densitometric parameters and that standardization is mandatory for obtaining comparable results. Ideally a threshold equal to the average of the densities of lung and soft tissue should be used, but -400 HU will do in a limited but common density range (-910 to -790 HU). For densitometry two erosions are recommended, for volumetry zero erosions should be used.

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Year:  1998        PMID: 9874837     DOI: 10.1118/1.598454

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


  9 in total

1.  Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Authors:  Sarah E Gerard; Jacob Herrmann; David W Kaczka; Guido Musch; Ana Fernandez-Bustamante; Joseph M Reinhardt
Journal:  Med Image Anal       Date:  2019-11-07       Impact factor: 8.545

2.  A generic approach to pathological lung segmentation.

Authors:  Awais Mansoor; Ulas Bagci; Ziyue Xu; Brent Foster; Kenneth N Olivier; Jason M Elinoff; Anthony F Suffredini; Jayaram K Udupa; Daniel J Mollura
Journal:  IEEE Trans Med Imaging       Date:  2014-07-08       Impact factor: 10.048

Review 3.  Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

Authors:  Awais Mansoor; Ulas Bagci; Brent Foster; Ziyue Xu; Georgios Z Papadakis; Les R Folio; Jayaram K Udupa; Daniel J Mollura
Journal:  Radiographics       Date:  2015 Jul-Aug       Impact factor: 5.333

4.  Target definition of moving lung tumors in positron emission tomography: correlation of optimal activity concentration thresholds with object size, motion extent, and source-to-background ratio.

Authors:  Adam C Riegel; M Kara Bucci; Osama R Mawlawi; Valen Johnson; Moiz Ahmad; Xiaojun Sun; Dershan Luo; Adam G Chandler; Tinsu Pan
Journal:  Med Phys       Date:  2010-04       Impact factor: 4.071

5.  Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach.

Authors:  Heewon Chung; Hoon Ko; Se Jeong Jeon; Kwon-Ha Yoon; Jinseok Lee
Journal:  IEEE J Transl Eng Health Med       Date:  2018-05-18       Impact factor: 3.316

6.  Texture-based Quantification of Centrilobular Emphysema and Centrilobular Nodularity in Longitudinal CT Scans of Current and Former Smokers.

Authors:  Shoshana B Ginsburg; Jason Zhao; Stephen Humphries; Sungshick Jou; Kunihiro Yagihashi; David A Lynch; Joyce D Schroeder
Journal:  Acad Radiol       Date:  2016-08-27       Impact factor: 3.173

7.  Automated segmentation of lungs with severe interstitial lung disease in CT.

Authors:  Jiahui Wang; Feng Li; Qiang Li
Journal:  Med Phys       Date:  2009-10       Impact factor: 4.071

8.  Automated texture-based quantification of centrilobular nodularity and centrilobular emphysema in chest CT images.

Authors:  Shoshana B Ginsburg; David A Lynch; Russell P Bowler; Joyce D Schroeder
Journal:  Acad Radiol       Date:  2012-10       Impact factor: 3.173

9.  Defining internal target volume using positron emission tomography for radiation therapy planning of moving lung tumors.

Authors:  Adam C Riegel; M Kara Bucci; Osama R Mawlawi; Moiz Ahmad; Dershan Luo; Adam Chandler; Tinsu Pan
Journal:  J Appl Clin Med Phys       Date:  2014-01-06       Impact factor: 2.102

  9 in total

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