Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection

False positive reduction is a key procedure of computer-aided pulmonary nodule detection. The goal is to recognize the true pulmonary nodule from the plentiful candidates, which received from the first step of pulmonary nodule candidate detection. Convolutional networks can be used to perform false positive nodule reduction, but the classification accuracy need to be further improved. Recently, residual network is more and more popular around the world with its distinguished performance. A multicontext three-dimensional residual convolutional neural network (3D Res-CNN) was proposed to realize the reduction of the false positive nodule. Using two scales of network to adapt the variation of pulmonary nodule size, instead of using an unreferenced function with reference to the identity mapping, 3D Res-CNN uses a shortcut connection to realize the residual structure. For alleviating the data imbalance, firstly patches are rotated and resampled in original images; secondly weights are allotted for different labels in the calculation of cost function. Experiments on volumetric computed tomography (CT) data indicate that our method gets state of the art performance: 0.843 average sensitivity with 0.125, 0.25, 0.5, 1, 2, 4, and 8 false positive per subject. The results show the effectiveness of residual convolutional network for the recognition of the true pulmonary nodule from the plentiful candidates.