AANet: Artery-Aware Network for Pulmonary Embolism Detection in CTPA Images

Jia Guo, Xinglong Liu, Yinan Chen, Shaoting Zhang, Guangyu Tao, Hong Yu, Huiyuan Zhu, Wenhui Lei, Huiqi Li, Na Wang

Abstract: Pulmonary embolism (PE) is life-threatening and computed tomography pulmonary angiography (CTPA) is the best diagnostic techniques in clinics. However, PEs usually appear as dark spots among the bright regions of blood arteries in CTPA images, which can be very similar with veins that are less bright and soft tissues. Even for experienced radiologists, the evaluation of PEs in CTPA is a time-consuming and nontrivial task. In this paper, we propose an artery-aware 3D fully convolutional network (AANet) that encodes artery information as the prior knowledge to detect arteries and PEs at the same time. In our approach, the artery context fusion block (ACF) is proposed to combine the multi-scale feature maps and generate both local and global contexts of vessels as soft attentions to precisely recognize PEs from soft tissues or veins. We evaluate our methods on the CAD-PE dataset with the artery and vein vessel labels. The experimental results with the sensitivity of 78.1%, 84.2%, and 85.1% at one, two, and four false positives per scan have been achieved, which shows that our method achieves state-of-the-art performance and demonstrate promising assistance for diagnosis in clinical practice.