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Abstract #3812

Deep Learning based spine labeling with three-plane 2D localizers without vertebrae segmentation

Dattesh Dayanand Shanbhag1, Arathi Sreekumari1, Soumya Ghose2, Chitresh Bhushan2, and Uday Patil3
1GE Healthcare, Bangalore, India, 2GE Global Research, Niskayuna, NY, United States, 3General Electric Company, Bangalore, India

In this work , we describe a deep learning-based methodology to generate vertebrae labels directly from the standard 2D tri-planar localizer images without the need any additional scanning or explicitly segmenting the vertebrae. This is accomplished by using deep-learning setup a to identify vertebrae labels directly on the localizer images. The method is demonstrated on lumbar spine localizer data to identify Thoracic-12 (T12), Lumbar-4 (L4) , and Sacral-1 (S1) vertebrae locations. In a test cohort of 50 lumbar MR spine exams, we report labeling accuracy of 92%, 98% and 96% for T12, L4 and S1 vertebrae respectively on localizer images.

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