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Abstract #4059

Towards Clinical Translation of Fully Automatic Segmentation and 3D Biomarker Extraction of Lumbar Spine MRI

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

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.

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