Affiliations: [a] School of Computer Science, Northwestern Polytechnical University, Xi’an, P. R. China | [b] School of Software & Microelectronics, Northwestern Polytechnical University, Xi’an, P. R. China | [c] School of Computer Science and Technology, Xidian University, Xi’an, P. R. China
Correspondence:
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Corresponding author. Gang Sha, School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, P. R. China. E-mail: [email protected].
Abstract: Purpose:Reading spinal CT (Computed Tomography) images is very important in the diagnosis of spondylosis, which is time-consuming and prones to make biases. In this paper, we propose a framework based on Faster-RCNN to improve detection performances of three spinal fracture lesions: cfracture (cervical fracture), tfracture (thoracic fracture) and lfracture (lumbar fracture). Methods:First, we use ResNet50 to replace VGG16 in backbone network in Faster-RCNN to increase depth of training network. Second, we utilize soft-NMS (Non-Maximum Suppression) instead of NMS to avoid missed detection of overlapped lesions. Third, we simplify RPN (Region Proposal Network) to accelerate training speed and reduce missed detection. Finally, we modify the classifier layer in Faster-RCNN and choose appropriate length-width ratio by changing anchor sizes in sliding window, then adopt multi-scale strategy in training to improve efficiency and accuracy. Results:The experimental results show that the proposed scheme has a good performance, mAP (mean average precision) is 90.6%, IOU (Intersection of Union) is 88.5 and detection time is 0.053 second per CT image, which means our proposed method can accurately detect spinal fracture lesions. Conclusion:Our proposed method can provide assistance and scientific references for both doctors and patients in clinically.
