Lung nodule detection in CT using 3D convolutional neural networks

Xiaojie Huang, Junjie Shan, Vivek Vaidya
2017 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)  
We propose a new computer-aided detection system that uses 3D convolutional neural networks (CNN) for detecting lung nodules in low dose computed tomography. The system leverages both a priori knowledge about lung nodules and confounding anatomical structures and data-driven machine-learned features and classifier. Specifically, we generate nodule candidates using a local geometric-model-based filter and further reduce the structure variability by estimating the local orientation. The nodule
more » ... didates in the form of 3D cubes are fed into a deep 3D convolutional neural network that is trained to differentiate nodule and non-nodule inputs. We use data augmentation techniques to generate a large number of training examples and apply regularization to avoid overfitting. On a set of 99 CT scans, the proposed system achieved state-of-the-art performance and significantly outperformed a similar hybrid system that uses conventional shallow learning. The experimental results showed benefits of using a priori models to reduce the problem space for datadriven machine learning of complex deep neural networks. The results also showed the advantages of 3D CNN over 2D CNN in volumetric medical image analysis.
doi:10.1109/isbi.2017.7950542 dblp:conf/isbi/HuangSV17 fatcat:on4ufzvmmvddhkq6rpb6vupt7a