Automated Spinal Midline Delineation on Biplanar X-Rays Using Mask R-CNN

Abstract

Manually annotating medical images with few landmarks to initialize 3D shape models is a common practice. For instance, when reconstructing the 3D spine from biplanar X-rays, the spinal midline, passing through vertebrae body centers (VBCs) and endplate midpoints, is required. This paper presents an automated spinal midline delineation method on frontal and sagittal views by using Mask R-CNN. The network detects all vertebrae from C7 to L5, followed by vertebrae segmentation and classification at the same time. After postprocessing to discard outliers, the vertebrae mask centers were regarded as VBCs to get the spine midline by polynomial fitting. Evaluation of the spinal midline on 136 images used root mean square error (RMSE) with respect to manual ground-truth. The RMSE ± standard error values of predicted spinal midlines (C7-L5) were 1.11 mm ± 0.67 mm on frontal views and 1.92mm ± 1.38 mm on sagittal views. The proposed method is capable of delineating spinal midlines on patients with different spine deformity degrees.