An Improved Faster R-CNN for Pulmonary Embolism Detection From CTPA Images

Abstract

Computer-aided detection of pulmonary embolism is an important technology method for diagnosing pulmonary embolism, which can help doctors diagnose quickly and save a lot of manpower. However, due to the small area of pulmonary embolism in the Computed Tomography Pulmonary Angiography (CTPA) slice images, some previous methods for detecting pulmonary embolism have a high number of false detection and missed detection. This study proposes a detection method of pulmonary embolism based on the improved faster region-based convolutional neural network (Faster R-CNN) named More Accurate Faster R-CNN (MA Faster R-CNN). A new feature fusion network named Multi-scale Fusion Feature Pyramid Network (MF-FPN) is proposed by extending and adding two bottom-up paths on the Feature Pyramid Network (FPN). It enhances the feature extraction capability of the entire network by transmitting low-level accurate location information, and makes up for the original information lost after multiple down-sampling, strengthens the use of detailed information, which is more helpful to the detection of small object. In the prediction module, the residual block is added before the fully-connected layer to deepen the network and enhance the classification accuracy, named residual prediction module (RPM). Compared with the original Faster R-CNN, the proposed MA Faster R-CNN which combines MF-FPN and RPM has a higher detection precision and solves the problems of false detection and missed detection of pulmonary embolism effectively. The average precision (AP) reached 85.88% on the CTPA pulmonary embolism dataset used in this article.

Keywords

• Faster R-CNN
• multi-scale fusion FPN
• residual prediction module
• pulmonary embolism