Automatic Lung Nodule Detection in Thoracic CT Scans Using Dilated Slice-Wise Convolutions.

PURPOSE Most state-of-the-art automated medical image analysis methods for volumetric data rely on adaptations of two-dimensional (2-D) and three-dimensional (3-D) Convolutional Neural Networks (CNNs). In this paper, we develop a novel unified CNN-based model that combines the benefits of 2D and 3D networks for analyzing volumetric medical images. METHODS In our proposed framework, multi-scale contextual information is first extracted from 2-D slices inside a Volume of Interest (VOI). This is followed by dilated 1-D convolutions across slices to aggregate in-plane features in a slice-wise manner and encode the information in the entire volume. Moreover, we formalize a curriculum learning strategy for a two-stage system (i.e. a system that consists of screening and false positive reduction), where the training samples are presented to the network in a meaningful order to further improve the performance. RESULTS We evaluated the proposed approach by developing a Computer-Aided Detection (CADe) system for lung nodules. Our results on 888 CT exams demonstrate that the proposed approach can effectively analyze volumetric data by achieving a sensitivity of > 0.99 in the screening stage and a sensitivity of > 0.96 at 8 false positives per case in the false positive reduction stage. CONCLUSION Our experimental results show that the proposed method provides competitive sresults compared to state-of-the-art 3-D frameworks. In addition, we illustrate the benefits of curriculum learning strategies in two-stage systems that are of common use in medical imaging applications.