Abstract:
Lumbar Spine plays a very important role in our load transfer and mobility. Vertebrae
localization and segmentation are useful in detecting spinal deformities and fractures.
Understanding of automated medical image is of main importance that will help the doctors
in handling the time consuming manual or semi manual diagnosis. Our thesis presents the
methods, that will help the clinicians to grade the severity of the disease with confidence, as
the current manual diagnoses by different doctors has dissimilarity and variations in the
analysis of diseases. In this research we are discussing the lumbar spine localization and
segmentation which help for the analysis of lumbar spine deformities. Lumber Spine is
localized using YOLOv5 which is the fifth variant of YOLO family. It is the fastest and the
lightest weight object detector. Mean Average Precision (mAP) of 0.975 is achieved by
YOLOv5. To diagnose the Lumbar Lordosis, we have correlated the angles with region area
that is computed from the YOLOv5 centroids and got 74.5% accuracy. Cropped images from
YOLOv5 bounding boxes are passed through Hed U-Net which is combination of
segmentation and edge detection frameworks, to get the segmented vertebrae and its edges.
Lumbar Lordortic Angles (LLA) and Lumbosacral Angles (LSA) are found after detecting
the corners of vertebrae using Harris Corner Detector with a very less mean error of 0.29˚ and
0.38˚ respectively. Lumbar spine is segmented using three deep neural models, the best
results are achieved by U-Net with highest dice coefficient score (DC) and Intersection over
Union (IOU). This thesis compares the results of three deep learning models that are Fully
Convolutional framework, U-Net and SegNet architecture.
