Scoliosis is a highly prevalent spine deformity that has traditionally been diagnosed
through measurement of the Cobb angle on radiographs. More recent technology such
as the commercial EOS imaging system, although more accurate, also require manual
intervention for selecting the extremes of the vertebrae forming the Cobb angle. This
results in a high degree of inter and intra observer error in determining the extent
of spine deformity. Our primary focus is to eliminate the need for manual intervention
by robustly quantifying the curvature of the spine in three dimensions, making it
consistent across multiple observers. Given the vertebrae centroids, the proposed
Vertebrae Sequence Angle (VSA) estimation and segmentation algorithm finds the largest
angle between consecutive pairs of centroids within multiple inflection points on
the curve. To exploit existing clinical diagnostic standards, the algorithm uses a
quasi-3-dimensional approach considering the curvature in the coronal and sagittal
projection planes of the spine. Experiments were performed with manually annotated
ground-truth classification of publicly available, centroid-annotated CT spine datasets.
This was compared with the results obtained from manual Cobb and Centroid angle estimation
methods. Using the VSA, we then automatically classify the occurrence and the severity
of spine curvature based on Lenke's classification for idiopathic scoliosis. We observe
that the results appear promising with a scoliotic angle lying within +/- 9 degrees
of the Cobb and Centroid angle, and vertebrae positions differing by at the most one
position. Our system also resulted in perfect classification of scoliotic from healthy
spines with our dataset with six cases.
