3D convolutional neural network for automatic detection of lung nodules in chest CT

Sardar Hamidian, Berkman Sahiner, Nicholas Petrick, Aria Pezeshk, Nicholas A. Petrick, Samuel G. Armato
2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has
more » ... fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.
doi:10.1117/12.2255795 pmid:28845077 pmcid:PMC5568782 dblp:conf/micad/HamidianSPP17 fatcat:jeqnjcvanjaodjr7ammori3shq