Autonomous image segmentation and identification of anatomical landmarks from lumbar spine intraoperative computed tomography scans using machine learning: A validation study

PURPOSE: Machine-learning algorithms are a subset of artificial intelligence that have proven to enhance analytics in medicine across various platforms. Spine surgery has the potential to benefit from improved hardware placement utilizing algorithms that autonomously and accurately measure pedicle and vertebral body anatomy. The purpose of this study was to assess the accuracy of an autonomous convolutional neural network (CNN) in measuring vertebral body anatomy utilizing clinical lumbar computed tomography (CT) scans and automatically segment vertebral body anatomy. METHODS: The CNN was trained utilizing 8000 manually segmented CT slices from 15 cadaveric specimens and 30 adult diagnostic scans. Validation was performed with twenty randomly selected patient datasets. Anatomic landmarks that were segmented included the pedicle, vertebral body, spinous process, transverse process, facet joint, and lamina. Morphometric measurement of the vertebral body was compared between manual measurements and automatic measurements. RESULTS: Automatic segmentation was found to have a mean accuracy ranging from 96.38% to 98.96%. Coaxial distance from the lamina to the anterior cortex was 99.10% with pedicle angulation error of 3.47%. CONCLUSION: The CNN algorithm tested in this study provides an accurate means to automatically identify the vertebral body anatomy and provide measurements for implants and placement trajectories.