Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.

Journal: Medical physics

Published Date: Jun 1, 2016

Abstract

PURPOSE: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs).

Authors

Atsushi Teramoto

Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi 470-1192, Japan.
Hiroshi Fujita

Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Osamu Yamamuro

East Nagoya Imaging Diagnosis Center, 3-4-26 Jiyugaoka, Chikusa-ku, Nagoya, Aichi 464-0044, Japan.
Tsuneo Tamaki

East Nagoya Imaging Diagnosis Center, 3-4-26 Jiyugaoka, Chikusa-ku, Nagoya, Aichi 464-0044, Japan.

Keywords

Female Humans Lung Lung Neoplasms Male Neural Networks, Computer Positron Emission Tomography Computed Tomography Radiographic Image Interpretation, Computer-Assisted ROC Curve Sensitivity and Specificity Support Vector Machine Whole Body Imaging

External Resources

View on PubMed Access via DOI PubMed (27277030)

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.

Abstract

Authors

Keywords

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