Abstract #4058

Fully automatic detection and voxel-wise mapping of vertebral body Modic changes using deep convolutional neural networks

Kenneth T Gao^1,2,3, Radhika Tibrewala^1,2, Madeline Hess^1,2, Upasana Bharadwaj^1,2, Gaurav Inamdar^1,2, Cynthia T Chin¹, Valentina Pedoia^1,2, and Sharmila Majumdar^1,2

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ³University of California, Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, CA, United States

Modic changes are common degenerative lesions seen in spinal MRI and are strongly linked to lower back pain. However, detection of Modic changes suffers from poor inter-operator and inter-scanner reliabilities. We present a fully automatic, quantitative model that leverages deep learning and signal-based clustering for mapping Modic changes from clinically acquired MRI. The model achieves an identification rate of 85.7% and substantial agreement with radiologists. More importantly, the mapping technique classifies detected lesions on a voxel-wise basis, allowing for assessment of sensitive, local pathologies.

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