Anatomical Structure-Aware Pulmonary Nodule Detection via Parallel Multi-task RoI Head

Haoyi Tao, Yuanfang Qiao, Lichi Zhang, Yiqiang Zhan, Zhong Xue, Qian Wang

Abstract: Automatic and accurate pulmonary nodule detection from Computed Tomography (CT) scans plays a vital role in efficient pulmonary cancer screening. Although recent anchor-based methods using Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in this task, they still have some limitations. First, they do not utilize any prior information such as blood vessel segmentation from images, which can effectively help the detection task. Second, they do not integrate enough context information in the nodule classification branch of the detection framework. Third, the detection is generally achieved by using one single model, which may be insufficient to produce satisfactory results. To overcome these limitations, here we first extract anatomical structures from CT scans and propose a weighted training patch sampling method based on the anatomical structure information. Besides, we propose a parallel multi-task region-of-interest (RoI) head for nodule classification. The proposed method is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method achieves 98.1% in sensitivity at one false-positive per scan and 99.4% in sensitivity at two false-positives per scan.