Literature DB >> 23883209

Computer-aided segmentation and volumetry of artificial ground-glass nodules at chest CT.

Ernst Th Scholten1, Colin Jacobs, Bram van Ginneken, Martin J Willemink, Jan-Martin Kuhnigk, Peter M A van Ooijen, Matthijs Oudkerk, Willem P Th M Mali, Pim A de Jong.   

Abstract

OBJECTIVE: The purpose of this study was to investigate a new software program for semiautomatic measurement of the volume and mass of ground-glass nodules (GGNs) in a chest phantom and to investigate the influence of CT scanner, reconstruction filter, tube voltage, and tube current.
MATERIALS AND METHODS: We used an anthropomorphic chest phantom with eight artificial GGNs with two different CT attenuations and four different volumes. CT scans were obtained with four models of CT scanner at 120 kVp and 25 mAs with a soft and a sharp reconstruction filter. On the 256-MDCT scanner, the tube current-exposure time product and tube voltage settings were varied. GGNs were measured with software that automatically segmented the nodules. Absolute percentage error (APE) was calculated for volume, mass, and density. Wilcoxon signed rank, Mann-Whitney U, and Kruskal-Wallis tests were used for analysis.
RESULTS: Volume and mass did not differ significantly from the true values. When measurements were expressed as APE, the error range was 2-36% for volume and 5-46% for mass, which was significantly different from no error. We did not find significant differences in APE between CT scanners with filters for lower tube current for volume or lower tube voltage for mass.
CONCLUSION: Computer-aided segmentation and mass and volume measurements of GGNs with the prototype software had promising results in this study.

Mesh:

Year:  2013        PMID: 23883209     DOI: 10.2214/AJR.12.9640

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  15 in total

1.  Interscan variation of semi-automated volumetry of subsolid pulmonary nodules.

Authors:  Ernst Th Scholten; Pim A de Jong; Colin Jacobs; Bram van Ginneken; Sarah van Riel; Martin J Willemink; Rozemarijn Vliegenthart; Matthijs Oudkerk; Harry J de Koning; Nanda Horeweg; Mathias Prokop; Willem P Th M Mali; Hester A Gietema
Journal:  Eur Radiol       Date:  2014-11-21       Impact factor: 5.315

2.  Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation.

Authors:  Ernst Th Scholten; Colin Jacobs; Bram van Ginneken; Sarah van Riel; Rozemarijn Vliegenthart; Matthijs Oudkerk; Harry J de Koning; Nanda Horeweg; Mathias Prokop; Hester A Gietema; Willem P Th M Mali; Pim A de Jong
Journal:  Eur Radiol       Date:  2014-10-07       Impact factor: 5.315

3.  Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network.

Authors:  Xiaoguang Tu; Mei Xie; Jingjing Gao; Zheng Ma; Daiqiang Chen; Qingfeng Wang; Samuel G Finlayson; Yangming Ou; Jie-Zhi Cheng
Journal:  Sci Rep       Date:  2017-09-01       Impact factor: 4.379

4.  Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.

Authors:  Ryoji Mikayama; Takashi Shirasaka; Tsukasa Kojima; Yuki Sakai; Hidetake Yabuuchi; Masatoshi Kondo; Toyoyuki Kato
Journal:  Br J Radiol       Date:  2021-12-15       Impact factor: 3.039

5.  Pulmonary adenocarcinomas presenting as ground-glass opacities on multidetector CT: three-dimensional computer-assisted analysis of growth pattern and doubling time.

Authors:  Andrea Borghesi; Davide Farina; Silvia Michelini; Matteo Ferrari; Diego Benetti; Simona Fisogni; Andrea Tironi; Roberto Maroldi
Journal:  Diagn Interv Radiol       Date:  2016 Nov-Dec       Impact factor: 2.630

6.  Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study.

Authors:  Marios A Gavrielides; Benjamin P Berman; Mark Supanich; Kurt Schultz; Qin Li; Nicholas Petrick; Rongping Zeng; Jenifer Siegelman
Journal:  Quant Imaging Med Surg       Date:  2017-12

7.  Anatomy packing with hierarchical segments: an algorithm for segmentation of pulmonary nodules in CT images.

Authors:  Chi-Hsuan Tsou; Kuo-Lung Lor; Yeun-Chung Chang; Chung-Ming Chen
Journal:  Biomed Eng Online       Date:  2015-05-14       Impact factor: 2.819

8.  Quantitative volumetry of ground-glass nodules on high-spatial-resolution CT with 0.25-mm section thickness and 1024 matrix: Phantom and clinical studies.

Authors:  Yuriko Yoshida; Masahiro Yanagawa; Akinori Hata; Yukihisa Sato; Mitsuko Tsubamoto; Shuhei Doi; Kazuki Yamagata; Tomo Miyata; Noriko Kikuchi; Noriyuki Tomiyama
Journal:  Eur J Radiol Open       Date:  2021-06-01

9.  Semi-automatic quantification of subsolid pulmonary nodules: comparison with manual measurements.

Authors:  Ernst Th Scholten; Bartjan de Hoop; Colin Jacobs; Saskia van Amelsvoort-van de Vorst; Rob J van Klaveren; Matthijs Oudkerk; Rozemarijn Vliegenthart; Harry J de Koning; Carlijn M van der Aalst; Willem Th M Mali; Hester A Gietema; Mathias Prokop; Bram van Ginneken; Pim A de Jong
Journal:  PLoS One       Date:  2013-11-21       Impact factor: 3.240

10.  Subsolid pulmonary nodule morphology and associated patient characteristics in a routine clinical population.

Authors:  Onno M Mets; Pim A de Jong; Ernst Th Scholten; Kaman Chung; Bram van Ginneken; Cornelia M Schaefer-Prokop
Journal:  Eur Radiol       Date:  2016-06-02       Impact factor: 5.315

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