Lumbar spine segmentation serves as an important first step for automated disease classification and monitoring, but manual segmentation is costly and time consuming. We present a deep learning-based pipeline to automatically segment the vertebral bodies, intervertebral discs, and paraspinal muscles in the lumbar spine. We leverage the results of this method to quickly and accurately extract disc height with a mean absolute error of 2.09 mm, muscle CSA with mean absolute errors of less than 1.46 cm2, and muscle centroid position with a mean absolute error of less than 7.23mm.
This abstract and the presentation materials are available to members only; a login is required.