A novel segmentation method for cervical vertebrae based on PointNet++ and converge segmentation

Abstract

BackgroundCervical spine instability is the key pathogenic factor for cervical spondylosis, which may easily cause cervical spinal cord nerve compression, numbness, weakness, and even paralysis of the limbs. The reconstruction of the internal fixation of the cervical spine is of great therapeutic significance, but is a high-risk and difficult procedure that requires precise planning. The high similarities between vertebrae may interfere with automatic operation planning; therefore, the segmentation of vertebrae is of great significance. MethodsOur segmentation algorithm has 3 parts. Firstly, an adaptive threshold filter to segment the cervical vertebra tissue structure form CT images. Secondly, segmentation of single vertebrae based on PointNet++ is introduced to segmentation cervical spine. Finally, converge segmentation which is based on edge information is utilized to clearly distinguish the edges of the two vertebrae to enhance the accuracy segmentation result. ResultsOur approach improved the accuracy of the system up to 96.15%, and achieved the highest reported average score based on this dataset. We compared the results of the CNN and PointNet methods on a separate dataset of 240 CT scans with 18 classes and achieved a significantly higher performance for any given vertebra. Our experiments illustrated the promise and robustness of recent PointNet++-based segmentation of medical images. ConclusionThe proposed method has better classification performance for segmentation cervical spine images, which segment a three-dimensional vertebral body directly and effectively. Furthermore, the precise segmentation of a single vertebral body can be used in automatic biomechanical analysis, computer-aided diagnosis and other aspects, so as to improve the level of automation in the treatment of cervical spondylosis.