Abstract:
Human spine is a complex structure that plays a vital role in the movement, protection,
and support of the body so it is very important to follow proper spine bio-mechanics to
avoid any unwanted effect on body. Spinal diseases can cause compression or pulling
the nerve roots, which can lead to radicular symptoms like back pain or leg pain. On the
other hand, it may cause deformities which are most common at C4-C7 and L4-S1 level.
Localization of vertebra bones that make up the spine is key in spinal disease diagnosis
such as calculating cobb angles, shape detection, detecting vertebra fractures and other
abnormalities. In this paper, we have covered four modules, first, for the vertebrae localization
we used detection transformer to localize 68 corner points, Secondly, we have
used a SegFormer to do the segmentation of the spine. Thirdly, center profile of the spine
was generated using center point technique for localization and morphological thinning
for segmentation, In the final step of shape analysis process, we take the profile of spine
to calculates the features and classify the data into normal, Single-bend (C-shaped) and
Double bend (S-shaped) spine. DETR gives mAP value of 0.96 at 0.5 IOU threshold and
SegFormer achieves a dice score of 0.93 in segmenting spinal images. For the classification
of the data, we have used different classifier (SVM, RF, KNN and NB). We have
obtained identical features from both Segformer and DETR techniques and compared the
results. Features acquired from localization technique(DETR) yield better accuracy when
using a random forest classifier.Random forest performs best for AASCE MICCAI 2019
dataset with an accuracy of 98.9%.
