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 a 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.
Sardar Hamidian, Berkman Sahiner, Nicholas Petrick, and Aria Pezeshk, "3D convolutional neural network for automatic detection of lung nodules in chest CT," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013409 (Presented at SPIE Medical Imaging: February 13, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2255795.
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