Madeline Hess1, Kenneth Gao1, Radhika Tibrewala1, Gaurav Inamdar1, Upasana Bharadwaj1, Cynthia Chin1, Valentina Pedoia1, and Sharmila Majumdar1
1Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, United States
1Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, United States
We present a deep learning-based pipeline to automatically segment the vertebral bodies, intervertebral discs, and paraspinal muscles in the lumbar spine. Using this method, we accurately and automatically extract disc height, muscle CSA, and centroid position for each structure.
Figure 1: Visualization of segmentation results from each Network.
The first, second and third columns show examples of vertebral body, intervertebral disc, and paraspinal muscle segmentation results, respectively.
Figure 3: Correlation (left column) and agreement (right column) between muscle CSA from manual versus inferred segmentations on each paraspinal muscle.
Agreement is displayed using Bland-Altman plots for CSA on each disc. Correlation between CSA from manual versus inferred muscle segmentations is displayed using a scatter plot, where the line x=y is indicated in grey and each point is the CSA calculated on each respective muscle (both left and right) on each slice in each patient.
