Using 1D Patch-Based Signatures for Efficient Cascaded Classification of Lung Nodules

In the last years, convolutional neural networks (CNN) have been largely used to address a wide range of image analysis problems. In medical imaging, their importance increased exponentially despite of known difficulties in building large annotated training datasets in medicine. When it comes to 3D image exams analysis, 3D convolutional networks commonly represent the state-of-art, but can easily became computationally prohibitive due to the massive amount of data and processing involved. This scenario creates opportunities for methods that deliver competitive results while promoting efficiency in data usage and processing time. In this context, this paper proposes a comprehensive 1D patch-based data representation model to be used in an efficient cascaded approach for lung nodules false positive reduction. The proposed pipeline combines three convolutional networks: a 3D network that uses regular multi-scale volumetric patches, a 2D network that uses a trigonometric bi-dimensional representation of these patches, and a 1D network that uses a very compact 1D patch representation for filtering obvious cases. We run our experiments using the publicly available LUNA challenge dataset and demonstrate that the proposed cascaded approach achieves very competitive results while using up to 55 times less data in average and running around 3.5 times faster in average when compared to regular 3D CNNs.