Article Figures & Data
Figures
- Figure 1
Example of segmentation of vertebrae, intervertebral discs, and dural sac on (a) sagittal and (b) axial MRI images and a corresponding (c) segmented 3-dimensional anatomical model. MRI, magnetic resonance imaging.
- Figure 2
Positive and negative identifications of central canal stenosis at each disc level.
- Figure 3
Representative (a) sagittal and (b) axial segmentation predictions of lumbar spine matching patient's MRI images in Figure 1. MRI, magnetic resonance imaging.
- Figure 4
High-level architectural diagram of the implemented deep learning algorithm used to generate automated MRI reports. MRI, magnetic resonance imaging.
- Figure 5
Exemplary L1-L2 MRI (a) sagittal and (b) axial images used for diagnostic assessment with the segmentation algorithm on levels known to have no disc bulging, no central canal stenosis, and no foraminal narrowing. Another example L1-L2 MRI (c) axial and (d) sagittal images of a diagnostic assessment using the segmentation algorithm in which the training radiologist labeled the disc to have a posterior disc protrusion abutting the thecal sac and compromising the neural foramina bilaterally.
- Figure 6
Graphic depiction of training convergence of the foraminal stenosis detector. In the top panel (6a), the x-axis is the number of training steps and the y-axis is the binary accuracy. The bottom panel (6b) shows a plot of binary cross-entropy (y-axis) versus number of training steps (x-axis). The binary cross-entropy is used to estimate error between the radiologist reads and the artificial intelligence predictions. Hence, decreasing binary cross-entropy is associated with desired accuracy gains.
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