  Segmentation and labeling of vertebrae in MRI images of the spine are
critical for the diagnosis of illnesses and abnormalities. These steps are
indispensable as MRI technology provides detailed information about the tissue
structure of the spine. Both supervised and unsupervised segmentation methods
exist, yet acquiring sufficient data remains challenging for achieving high
accuracy. In this study, we propose an enhancing approach based on modified
attention U-Net architecture for panoptic segmentation of 3D sliced MRI data of
the lumbar spine. Our method achieves an impressive accuracy of 99.5\% by
incorporating novel masking logic, thus significantly advancing the
state-of-the-art in vertebral segmentation and labeling. This contributes to
more precise and reliable diagnosis and treatment planning.
