RESUMO
Objective To propose a new rib fracture detection network Rib-Net to automatically and accurately detect and locate rib fracture and address the issue of missed diagnosis of rib fractures. Methods The public data set RibFrac Dataset was used to evaluate the performance of the Rib-Net, and the data set was divided into training set (420 cases), validation set (80 cases), and test set (160 cases). The Rib-Net was composed of the object detection integrated network Ensemble Detection Net (ED-Ne), Complete Box Fusion (CBF) module and the segmentation network 3D Unet. Firstly, Retina Unet, UFRCNN+ and Mask RCNN were integrated to form ED-Net to predict rib fracture candidate boxes. Secondly, a new CBF module was designed to fuse overlapping fracture candidate boxes to generate candidate boxes with accurate positioning and accurate confidence. Finally, Unet was used for rib fracture segmentation to achieve further precise localization of rib fractures. Results On the “MICCAI 2020 RibFrac Challenge: Rib Fracture Detection and Classification challenge”, our proposed Rib-Net’s detection results reached the best performance, and its recall rate, free-response receiver operating characteristic curve(FROC) value and Dice were 92.3%, 0.859 and 0.61, respectively. Conclusion The Rib-Net network can efficiently and accurately detect and locate rib fractures on chest CT images, effectively assisting doctors in making accurate diagnosis.