Towards Accurate Pulmonary Nodule Detection by Representing Nodules as Points With High-Resolution Network

Abstract

Almost all successful nodule detectors rely heavily on a fixed set of anchor boxes. In this paper, inspired by the success of the keypoint estimation method in natural image detection, we propose an anchor-free framework for accurate pulmonary nodule detection. We first present a novel representation for detecting nodules, in terms of their 3D center locations, which reduces the number of hyper-parameters and the corresponding computation related to anchors, thus making the nodule detection pipeline much simpler. Then, an effective two-stream network is introduced to reduce the false positive nodule candidates, by aggregating information from the image stream and motion-history stream. Experiments show that the proposed approach achieves a sensitivity of 96.1%, with 8 false positives per scan, and a CPM score of 90.6%, on the publicly available LUNA16 dataset, which outperforms other state-of-the-art methods. By testing on the SPIE-AAPM dataset with models pre-trained on the LUNA16, our proposed method yields 92.8% sensitivity with 8 false positives per scan. This demonstrates the effectiveness and generalization ability of our method.

Keywords

• Lung nodule detection
• 3D convolution neural network
• keypoint estimation