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Labeling vertebral discs from MRI scans is important for the proper diagnosisof spinal related diseases, including multiple sclerosis, amyotrophic lateralsclerosis, degenerative cervical myelopathy and cancer. Automatic labeling ofthe vertebral discs in MRI data is a difficult task because of the similaritybetween discs and bone area, the variability in the geometry of the spine andsurrounding tissues across individuals, and the variability across scans(manufacturers, pulse sequence, image contrast, resolution and artefacts). Inprevious studies, vertebral disc labeling is often done after a disc detectionstep and mostly fails when the localization algorithm misses discs or has falsepositive detection. In this work, we aim to mitigate this problem byreformulating the semantic vertebral disc labeling using the pose estimationtechnique. To do so, we propose a stacked hourglass network with multi-levelattention mechanism to jointly learn intervertebral disc position and theirskeleton structure. The proposed deep learning model takes into account thestrength of semantic segmentation and pose estimation technique to handle themissing area and false positive detection. To further improve the performanceof the proposed method, we propose a skeleton-based search space to reducefalse positive detection. The proposed method evaluated on spine generic publicmulti-center dataset and demonstrated better performance comparing to previouswork, on both T1w and T2w contrasts. The method is implemented in ivadomed(https://ivadomed.org).

Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling