Study on the detection of CT image based on multi-task network method of global attention for fine-fracture / 中国医学装备

ABSTRACT

Objective:To improve the perception of computed tomography(CT)images in detecting fine fracture through multi-task network of global attention,and to realize the detection of the target of fine fracture at case level through multi-task,and to quickly and accurately identify and locate fracture from a large number of CT images,so as to assist doctors to timely conduct treatment.Methods:A grouped Non-local network method was introduced to calculate the remote dependency relationship between each position of CT image continuous sections and channel.A single-stage detector of multi-objective detection model three dimension(3D)RetinaNet was integrated with the medical image semantic segmentation architecture(3D U-Net).A end-to-end multi-task 3D convolutional network was realized,which realized the detection of case level for fine fracture through multi-task collaboration.Select 600 CT scan images from the Rib Frac Dataset of rib fractures provided by the MICCAI 2020 Challenge,and they were divided into training set(500 cases)and test set(100 cases)as the ratio of 5:1 to test the precise performance of multi-task 3D convolutional network.Results:The precise performance of multi-task 3D convolutional network method was better than that of single-task FracNet,3D RetinaNet and 3D Retina U-Net in detection,which average precision was respectively higher 7.8%and 11.4%than 3D RetinaNet and 3D Retina U-Net.It was better than two kinds of single-task network detection method included 3D Faster R-CNN and 3D Mask R-CNN,and the average precision of that was respectively higher 6.7%and 3.1%than them.Conclusion:The integrated different modules of global attention multi-task network can improve the detection performance of fine fracture.The introduction of grouped Non-local network method can further improve the precise performance for the targets of fine fractures in detection.