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

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 i...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.105382-105392
Main Authors: Yuan, Hongfang, Shao, Yajun, Liu, Zhenhong, Wang, Huaqing
Format: Article
Language:English
Subjects:
Angiography
Arteries
Artificial neural networks
Computed tomography
Convolution
Faster R-CNN
Feature extraction
Lung
Manpower
Modules
multi-scale fusion FPN
Object detection
Proposals
Pulmonary diseases
pulmonary embolism
Pulmonary embolisms
residual prediction module
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3099479