Development and Validation of a Deep Learning Based Automated Lumbar Spinal Canal Segmentation and Measurement Tool
Poster Presentation at the European Congress of Radiology, Vienna, 2019
We describe a novel use-case of Convolutional Neural Networks to automatically measure spinal canal diameter and detect stenosis on
Methods and Materials
Axial T2 weighted MRI scans of the
We obtained a final DICE score of 98.8 at the level of normal inter-vertebral discs and 98.1 in those with pathology. The difference in predicted and radiologist-reported AP diameters was insignificant (p<0.01) in 16 cases. The model was also able to successfully detect all axial slices with disc herniation.
The model gave near radiologist-level performance in detecting stenosis and calculating the spinal canal diameter.