Synergistically segmenting and reducing fracture bones via whole-to-whole deep dense matching

Abstract

In bone fracture preoperative surgical planning, 3D fracture reduction is of great significance. However, segmenting 3D fracture bones into fragments and assembling the bone fragments to restore their original morphology are labor intensive and time consuming. Current works, however, omit their synergistic effects between reduction and segmentation, which, we envision, can significantly benefit the fracture reduction. To this end, a method is proposed to alternatively segment and assemble the fragments by incorporating the contralateral bone as template and leveraging the synergistic effect on segmentation and reduction. The 3D fracture model is initially segmented into separated outer surface of fragments. The reduction is then conducted by template matching, specifically, a whole-to-whole matching strategy through deep learning of dense features is employed. Thereafter, the segmentation is further refined with the assistance of the template. The process of reduction and segmentation is iterated until the algorithm converges. Experiments were conducted on simulated data and clinical data, the mean segmentation accuracy is 95.26%, the assembling translation error is 3.28 ± 3.01 mm, and the assembling rotation error is 1.62 ± 1.71°. Results demonstrated the superiority of our method over state-of-the-art methods, and it can be used as a feasible scheme for fracture reduction planning. Moreover, it is also proved that segmentation and reduction can promote each other.