对比多种深度学习模型，找出最适合腰椎间盘分割的算法。
Comprehensive Study on Lumbar Disc Segmentation Techniques Using MRI Data
- 用ResUnext、TransUNet等模型做腰椎核磁分割，比较效果。
- ResUnext表现最优，像素准确率0.9492，Dice系数0.8425。
- 滤波增强模型稳定性，尤其对Dense UNet提升明显。
腰椎间盘分割对脊柱疾病诊断与治疗至关重要，可精准识别医学影像中的椎间盘边界。深度学习的发展催生了多种分割方法，性能各异。本研究评估了ResUnext、Ef3 Net、UNet和TransUNet等先进深度学习架构在腰椎间盘分割中的表现，重点考察像素准确率（Pixel Accuracy）、平均交并比（Mean IoU）和Dice系数等指标。结果表明，ResUnext取得最高分割精度，像素准确率为0.9492，Dice系数达0.8425，TransUNet紧随其后。滤波技术提升了多数模型性能，尤其增强了Dense UNet的稳定性和分割质量。研究证实了这些模型在腰椎间盘分割中的有效性，并指出了未来改进方向。
原文摘要 · Abstract (English)
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.
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