A Two-Stage Convolutional Neural Network for Pulmonary Embolism Detection From CTPA Images

This paper presents a two-stage convolutional neural network (CNN) for automated detection of pulmonary embolisms (PEs) on CT pulmonary angiography (CTPA) images. The first stage utilizes a novel 3D candidate proposal network that detects a set of cubes containing suspected PEs from the entire 3D CTPA volume. In the second stage, each candidate cube is transformed to be aligned to the direction of the affected vessel and the cross-sections of the vessel-aligned cubes are input to a 2D classification network for false positive elimination. We have evaluated our approach using both the test dataset from the PE challenge and our own dataset consisting of 129 CTPA data with a total of 269 embolisms. The experimental results demonstrate that our method achieves a sensitivity of 75.4% at two false positives per scan at 0 mm localization error, which is superior to the winning system in the literature (i.e., sensitivity of 60.8% at the same level of false positives and localization error). On our own dataset, our method achieves sensitivities of 76.3%, 78.9%, and 84.2% at two false positives per scan at 0, 2, and 5 mm localization error, respectively.