Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions

Many lumbar spine diseases are caused by defects or degeneration of lumbar intervertebral
discs (IVD) and are usually diagnosed through inspection of the patient’s lumbar spine MRI.
Efficient and accurate assessments of the lumbar spine are essential but a challenge due to
the size of the clinical radiologist workforce not keeping pace with the demand for radiology
services. In this paper, we present a methodology to automatically annotate lumbar spine
IVDs with their height and degenerative state which is quantified using the Pfirrmann grading
system. The method starts with semantic segmentation of a mid-sagittal MRI image into six
distinct non-overlapping regions, including the IVD and vertebrae regions. Each IVD region
is then located and assigned with its label. Using geometry, a line segment bisecting the IVD
is determined and its Euclidean distance is used as the IVD height. We then extract an
image feature, called self-similar color correlogram, from the nucleus of the IVD region as a
representation of the region’s spatial pixel intensity distribution. We then use the IVD height
data and machine learning classification process to predict the Pfirrmann grade of the IVD.
We considered five different deep learning networks and six different machine learning algorithms
in our experiment and found the ResNet-50 model and Ensemble of Decision Trees
classifier to be the combination that gives the best results. When tested using a dataset containing
515 MRI studies, we achieved a mean accuracy of 88.1%.