Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: A preliminary study

Atsushi Teramoto, Hiroshi Fujita, Katsuaki Takahashi, Osamu Yamamuro, Tsuneo Tamaki, Masami Nishio, Toshiki Kobayashi

研究成果: Article

15 引用 (Scopus)

抄録

Purpose: In this study, an automated scheme for detecting pulmonary nodules using a novel hybrid PET/CT approach is proposed, which is designed to detect pulmonary nodules by combining data from both sets of images. Methods: Solitary nodules were detected on CT by a cylindrical filter that we developed previously, and in the PET imaging, high-uptake regions were detected automatically using thresholding based on standardized uptake values along with false-positive reduction by means of the anatomical information obtained from the CT images. Initial candidate nodules were identified by combining the results. False positives among the initial candidates were eliminated by a rule-based classifier and three support vector machines on the basis of the characteristic features obtained from CT and PET images. Results: We validated the proposed method using 100 cases of PET/CT images that were obtained during a cancer-screening program. The detection performance was assessed by free-response receiver operating characteristic (FROC) analysis. The sensitivity was 83.0 % with the number of false positives/case at 5.0, and it was 8 % higher than the sensitivity of independent detection systems using CT or PET images alone. Conclusion: Detection performance indicates that our method may be of practical use for the identification of pulmonary nodules in PET/CT images.

元の言語English
ページ(範囲)59-69
ページ数11
ジャーナルInternational Journal of Computer Assisted Radiology and Surgery
9
発行部数1
DOI
出版物ステータスPublished - 01-01-2014

Fingerprint

Positron emission tomography
Tomography
Support vector machines
Screening
Classifiers
Imaging techniques
Lung
Early Detection of Cancer
ROC Curve
Positron Emission Tomography Computed Tomography

All Science Journal Classification (ASJC) codes

  • Surgery
  • Radiology Nuclear Medicine and imaging
  • Health Informatics

これを引用

Teramoto, Atsushi ; Fujita, Hiroshi ; Takahashi, Katsuaki ; Yamamuro, Osamu ; Tamaki, Tsuneo ; Nishio, Masami ; Kobayashi, Toshiki. / Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography : A preliminary study. :: International Journal of Computer Assisted Radiology and Surgery. 2014 ; 巻 9, 番号 1. pp. 59-69.
@article{5220e7d54a664ad787fdb911ef945b27,
title = "Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: A preliminary study",
abstract = "Purpose: In this study, an automated scheme for detecting pulmonary nodules using a novel hybrid PET/CT approach is proposed, which is designed to detect pulmonary nodules by combining data from both sets of images. Methods: Solitary nodules were detected on CT by a cylindrical filter that we developed previously, and in the PET imaging, high-uptake regions were detected automatically using thresholding based on standardized uptake values along with false-positive reduction by means of the anatomical information obtained from the CT images. Initial candidate nodules were identified by combining the results. False positives among the initial candidates were eliminated by a rule-based classifier and three support vector machines on the basis of the characteristic features obtained from CT and PET images. Results: We validated the proposed method using 100 cases of PET/CT images that were obtained during a cancer-screening program. The detection performance was assessed by free-response receiver operating characteristic (FROC) analysis. The sensitivity was 83.0 {\%} with the number of false positives/case at 5.0, and it was 8 {\%} higher than the sensitivity of independent detection systems using CT or PET images alone. Conclusion: Detection performance indicates that our method may be of practical use for the identification of pulmonary nodules in PET/CT images.",
author = "Atsushi Teramoto and Hiroshi Fujita and Katsuaki Takahashi and Osamu Yamamuro and Tsuneo Tamaki and Masami Nishio and Toshiki Kobayashi",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/s11548-013-0910-y",
language = "English",
volume = "9",
pages = "59--69",
journal = "Computer-Assisted Radiology and Surgery",
issn = "1861-6410",
publisher = "Springer Verlag",
number = "1",

}

Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography : A preliminary study. / Teramoto, Atsushi; Fujita, Hiroshi; Takahashi, Katsuaki; Yamamuro, Osamu; Tamaki, Tsuneo; Nishio, Masami; Kobayashi, Toshiki.

:: International Journal of Computer Assisted Radiology and Surgery, 巻 9, 番号 1, 01.01.2014, p. 59-69.

研究成果: Article

TY - JOUR

T1 - Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography

T2 - A preliminary study

AU - Teramoto, Atsushi

AU - Fujita, Hiroshi

AU - Takahashi, Katsuaki

AU - Yamamuro, Osamu

AU - Tamaki, Tsuneo

AU - Nishio, Masami

AU - Kobayashi, Toshiki

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Purpose: In this study, an automated scheme for detecting pulmonary nodules using a novel hybrid PET/CT approach is proposed, which is designed to detect pulmonary nodules by combining data from both sets of images. Methods: Solitary nodules were detected on CT by a cylindrical filter that we developed previously, and in the PET imaging, high-uptake regions were detected automatically using thresholding based on standardized uptake values along with false-positive reduction by means of the anatomical information obtained from the CT images. Initial candidate nodules were identified by combining the results. False positives among the initial candidates were eliminated by a rule-based classifier and three support vector machines on the basis of the characteristic features obtained from CT and PET images. Results: We validated the proposed method using 100 cases of PET/CT images that were obtained during a cancer-screening program. The detection performance was assessed by free-response receiver operating characteristic (FROC) analysis. The sensitivity was 83.0 % with the number of false positives/case at 5.0, and it was 8 % higher than the sensitivity of independent detection systems using CT or PET images alone. Conclusion: Detection performance indicates that our method may be of practical use for the identification of pulmonary nodules in PET/CT images.

AB - Purpose: In this study, an automated scheme for detecting pulmonary nodules using a novel hybrid PET/CT approach is proposed, which is designed to detect pulmonary nodules by combining data from both sets of images. Methods: Solitary nodules were detected on CT by a cylindrical filter that we developed previously, and in the PET imaging, high-uptake regions were detected automatically using thresholding based on standardized uptake values along with false-positive reduction by means of the anatomical information obtained from the CT images. Initial candidate nodules were identified by combining the results. False positives among the initial candidates were eliminated by a rule-based classifier and three support vector machines on the basis of the characteristic features obtained from CT and PET images. Results: We validated the proposed method using 100 cases of PET/CT images that were obtained during a cancer-screening program. The detection performance was assessed by free-response receiver operating characteristic (FROC) analysis. The sensitivity was 83.0 % with the number of false positives/case at 5.0, and it was 8 % higher than the sensitivity of independent detection systems using CT or PET images alone. Conclusion: Detection performance indicates that our method may be of practical use for the identification of pulmonary nodules in PET/CT images.

UR - http://www.scopus.com/inward/record.url?scp=84895065936&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84895065936&partnerID=8YFLogxK

U2 - 10.1007/s11548-013-0910-y

DO - 10.1007/s11548-013-0910-y

M3 - Article

C2 - 23793722

AN - SCOPUS:84895065936

VL - 9

SP - 59

EP - 69

JO - Computer-Assisted Radiology and Surgery

JF - Computer-Assisted Radiology and Surgery

SN - 1861-6410

IS - 1

ER -