Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet

TL;DR

This paper introduces a modified attention U-Net model for panoptic segmentation and labeling of lumbar spine vertebrae in MRI images, achieving high accuracy and improving diagnostic reliability.

Contribution

The study presents a novel modified attention U-Net architecture with masking logic for 3D lumbar spine MRI segmentation, advancing current state-of-the-art methods.

Findings

Achieved 99.5% segmentation accuracy

Significantly improved vertebral labeling precision

Enhanced diagnosis and treatment planning

Abstract

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.