  The clinical treatment of degenerative and developmental lumbar spinal
stenosis (LSS) is different. Computed tomography (CT) is helpful in
distinguishing degenerative and developmental LSS due to its advantage in
imaging of osseous and calcified tissues. However, boundaries of the vertebral
body, spinal canal and dural sac have low contrast and hard to identify in a CT
image, so the diagnosis depends heavily on the knowledge of expert surgeons and
radiologists. In this paper, we develop an automatic lumbar spinal CT image
segmentation method to assist LSS diagnosis. The main contributions of this
paper are the following: 1) a new lumbar spinal CT image dataset is constructed
that contains 2393 axial CT images collected from 279 patients, with the ground
truth of pixel-level segmentation labels; 2) a dual densely connected U-shaped
neural network (DDU-Net) is used to segment the spinal canal, dural sac and
vertebral body in an end-to-end manner; 3) DDU-Net is capable of segmenting
tissues with large scale-variant, inconspicuous edges (e.g., spinal canal) and
extremely small size (e.g., dural sac); and 4) DDU-Net is practical, requiring
no image preprocessing such as contrast enhancement, registration and
denoising, and the running time reaches 12 FPS. In the experiment, we achieve
state-of-the-art performance on the lumbar spinal image segmentation task. We
expect that the technique will increase both radiology workflow efficiency and
the perceived value of radiology reports for referring clinicians and patients.
