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

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 posi...

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Vydáno v:	Medical physics (Lancaster) Ročník 43; číslo 6; s. 2821 - 2827
Hlavní autoři:	Teramoto, Atsushi, Fujita, Hiroshi, Yamamuro, Osamu, Tamaki, Tsuneo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States American Association of Physicists in Medicine 01.06.2016
Témata:	60 APPLIED LIFE SCIENCES Artificial neural networks Biological material, e.g. blood, urine; Haemocytometers cancer CAT SCANNING Computed tomography Computer aided diagnosis Computerised tomographs computerised tomography computer‐aided detection convolutional neural network Digital computing or data processing equipment or methods, specially adapted for specific applications false‐positive reduction feature extraction Female Humans Image data processing or generation, in general In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines Inference methods or devices lung Lung - diagnostic imaging Lung Neoplasms - diagnostic imaging Lungs Male Measuring half‐life of a radioactive substance medical image processing Medical image quality Medical image segmentation NEURAL NETWORKS Neural Networks (Computer) nodule PET/CT POSITRON COMPUTED TOMOGRAPHY positron emission tomography Positron emission tomography (PET) Positron Emission Tomography Computed Tomography - methods RADIATION PROTECTION AND DOSIMETRY Radiographic Image Interpretation, Computer-Assisted - methods Radiologists ROC Curve Scintigraphy SENSITIVITY Sensitivity and Specificity Support Vector Machine support vector machines Whole Body Imaging - methods lung false-positive reduction nodule computer-aided detection convolutional neural network PET/CT
ISSN:	0094-2405, 2473-4209, 2473-4209
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Popis
Shrnutí:	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). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using an active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.
Bibliografie:	teramoto@fujita‐hu.ac.jp Author to whom correspondence should be addressed. Electronic mail ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23
ISSN:	0094-2405 2473-4209 2473-4209
DOI:	10.1118/1.4948498