mlsanityData to 14 Oct 2026

Pioneering Precision in Lumbar Spine MRI Segmentation with Advanced Deep Learning and Data Enhancement

arXiv 2409.06018 ↗First posted 9 Sep 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accurately represent three critical classes: vertebrae, spinal canal, and intervertebral discs (IVDs). By rectifying class inconsistencies in the data preprocessing stage, the fidelity of the training data is ensured. The modified U-Net model incorporates innovative architectural enhancements, including an upsample block with leaky Rectified Linear Units (ReLU) and Glorot uniform initializer, to mitigate common issues such as the dying ReLU problem and improve stability during training. Introducing a custom combined loss function effectively tackles class imbalance, significantly improving segmentation accuracy. Evaluation using a comprehensive suite of metrics showcases the superior performance of this approach, outperforming existing methods and advancing the current techniques in lumbar spine segmentation. These findings hold significant advancements for enhanced lumbar spine MRI and segmentation diagnostic accuracy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 54 references that are in the map. First posted on arXiv 9 Sep 2024.

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References · 14

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  9. 2024A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practicecited by 26
  10. 2023Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Unioncited by 17
  11. 2018Why ReLU Units Sometimes Die: Analysis of Single-Unit Error Backpropagation in Neural Networkscited by 12
  12. 2021Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Lossescited by 12
2 more
  1. 2017Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Imagescited by 7
  2. 2020ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performancecited by 7

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