Pulmonary nodule type classification with convolutional networks

F. Ciompi, K. Chung, A.A. A. Setio, S. J van Riel, E. Th. Scholten, P.K. Gerke, C. Jacobs, U. Pastorino, A. Marchiano, M.M. W. Wille, M. Prokop and B. van Ginneken

in: Medical Image Computing and Computer-Assisted Intervention, 2016

Abstract

Classification of detected pulmonary nodules is a key task in deciding the optimal follow-up strategy for patients in lung cancer screening. We propose a framework based on Convolutional Networks (ConvNets) to automatically assess nodule type for lesions detected in CT scans. The proposed ConvNet processes nodules in 3D scans through a combination of several 2D views and classifies it as solid, part-solid, non-solid and calcified. We validated the method on data from the lung cancer screening trials DLCST and MILD.