In spine imaging, efficient automatic segmentation is crucial for clinical decision‐making, yet current models increase accuracy at the expense of elevated parameter counts and computational complexity, complicating integration with contemporary medical devices. Addressing identified challenges, this research introduces LE‐NeXt, a spine segmentation framework utilizing multi‐dimensional spatial attention and multi‐scale feature extraction, optimizing the architecture via convolution and MLP. It integrates lightweight convolutions and attention mechanisms within an encoder‐decoder model, enhancing stage‐specific feature extraction while ensuring efficiency. Experimental analyses on VerSe and SpineWeb datasets demonstrate that LE‐NeXt outperforms the lightweight U‐NeXt, enhancing IoU accuracy from 87.7 to 89.8 on VerSe, and exceeds the performance of established networks such as U‐Net and its variants. Significantly, on SpineWeb, LE‐NeXt not only surpasses Trans U‐Net in accuracy but also achieves a considerable reduction in both parameter count and computational complexity. These results emphasize LE‐NeXt's effectiveness in improving segmentation precision efficiently, optimally balancing computational efficiency and accuracy.

中文翻译：

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基于多尺度特征提取和多维空间注意力的高效脊柱分割网络

在脊柱成像中，高效的自动分割对于临床决策至关重要，但当前模型以增加参数数量和计算复杂性为代价来提高准确性，从而使与当代医疗设备的集成变得复杂。为了解决已发现的挑战，本研究引入了 LE-NeXt，这是一种利用多维空间注意力和多尺度特征提取的脊柱分割框架，通过卷积和 MLP 优化架构。它将轻量级卷积和注意力机制集成在编码器-解码器模型中，在确保效率的同时增强阶段特定的特征提取。对 VerSe 和 SpineWeb 数据集的实验分析表明，LE-NeXt 优于轻量级 U-NeXt，将 VerSe 上的 IoU 精度从 87.7 提高到 89.8，并超过了 U-Net 及其变体等已建立网络的性能。值得注意的是，在 SpineWeb 上，LE-NeXt 不仅在准确度上超越了 Trans U-Net，而且在参数数量和计算复杂度上都实现了大幅降低。这些结果强调了 LE-NeXt 在有效提高分割精度、优化平衡计算效率和准确性方面的有效性。