Comprehensive Study on Lumbar Disc Segmentation Techniques Using MRI Data

TL;DR

This study evaluates various deep learning models for lumbar disc segmentation in MRI images, finding ResUnext to be the most accurate, with filtering techniques further improving model performance.

Contribution

It provides a comparative analysis of advanced deep learning architectures for lumbar disk segmentation, highlighting the most effective models and techniques.

Findings

ResUnext achieved the highest accuracy with a Pixel Accuracy of 0.9492.

Filtering techniques improved segmentation stability and quality.

TransUNet closely followed ResUnext in performance.

Abstract

Lumbar disk segmentation is essential for diagnosing and curing spinal disorders by enabling precise detection of disk boundaries in medical imaging. The advent of deep learning has resulted in the development of many segmentation methods, offering differing levels of accuracy and effectiveness. This study assesses the effectiveness of several sophisticated deep learning architectures, including ResUnext, Ef3 Net, UNet, and TransUNet, for lumbar disk segmentation, highlighting key metrics like as Pixel Accuracy, Mean Intersection over Union (Mean IoU), and Dice Coefficient. The findings indicate that ResUnext achieved the highest segmentation accuracy, with a Pixel Accuracy of 0.9492 and a Dice Coefficient of 0.8425, with TransUNet following closely after. Filtering techniques somewhat enhanced the performance of most models, particularly Dense UNet, improving stability and segmentation quality. The findings underscore the efficacy of these models in lumbar disk segmentation and highlight potential areas for improvement.