Abstract
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). 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. 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. 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.
Highlights
Lung cancer is the leading cause of cancer-related deaths among men.1 early detection is essential for decreasing the number of cancer-related deaths
The authors’ ensemble false positives (FPs)-reduction method eliminated 93% of the FPs; their proposed method using convolutional neural networks (CNNs) technique eliminates approximately half the FPs existing in the previous study
In order to eliminate such FPs while maintaining the value of true positives (TPs), this study focused on CNN, which is a type of deep learning architecture
Summary
Lung cancer is the leading cause of cancer-related deaths among men. early detection is essential for decreasing the number of cancer-related deaths. PET/CT is an imaging technique that provides both metabolic and anatomical information; it is useful for the early detection of lung cancer. We focused on the automated detection of pulmonary nodules using PET/CT images. Many lung tumor segmentation methods have been proposed for schemes for PET/CT images.. Most of the methods described in these studies detected pulmonary nodules and masses from PET images alone; CT images were only used for identification of the lung region because the image quality of CT images for attenuation correction was insufficient. An automated scheme for the detection of pulmonary nodules making use of both CT and PET images was developed.. We propose an improved FP-reduction scheme for the detection of pulmonary nodules in PET/CT images. The detection performance as evaluated with the original PET/CT image database has been discussed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
