Purpose: In chest computed tomography (CT) scans, pulmonary vessel suppression can make pulmonary nodules more evident, and therefore may increase the detectability of early lung cancer. The purpose of this study was to develop a computer-aided detection (CAD) system with a vessel suppression function and to verify the effectiveness of the vessel suppression on the performance of the pulmonary nodule CAD system.
Methods: A CAD system with a vessel suppression function capable of suppressing vessels and detecting nodules was developed. First, a convolutional neural network (CNN)-based pulmonary vessel suppression technique was employed to remove the vessels from lungs while preserving the nodules. Then, a CNN-based pulmonary nodule detector was utilized to sequentially generate nodule candidates and reduce false positives (FPs). The performance levels of CAD systems with and without the vessel suppression function were compared using 888 three-dimensional chest CT scans from the Lung Nodule Analysis 2016 (LUNA16) dataset. The pulmonary nodule detection results were quantitatively evaluated using the average sensitivity at seven predefined FP rates: 0.125, 0.25, 0.5, 1, 2, 4, and 8 FPs per scan.
Results: The developed pulmonary nodule CAD system improved the average sensitivity to 0.977 from 0.950 owing to the addition of the vessel suppression function.
Conclusions: The vessel suppression function considerably improved the performance of the CAD system for pulmonary nodule detection. In practice, it would be embedded in CAD systems to assist radiologists in detecting pulmonary nodules in chest CT scans.
Keywords: computed tomography (CT); computer-aided detection (CAD); convolutional neural network (CNN); nodule detection; vessel suppression.
© 2020 American Association of Physicists in Medicine.