轻量级网络提升医学图像器官分割精度，适合资源受限的临床环境。
LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical Images
- 结合轻量卷积与分组移位块，降低参数量同时保持特征捕捉能力。
- 在UWMGI和MSD Colon数据集上优于多数主流分割模型。
- 动态损失加权机制提升训练稳定性，适合医疗场景部署。
UNet及其变体在医学图像分割中广泛应用，但其庞大的参数量和计算复杂度使其难以在计算资源有限的临床环境中使用。为此，我们提出一种新型轻量级移位U-Net（LSU-Net）。通过轻量化整合轻量卷积块（Light Conv Block）与分词移位块（Tokenized Shift Block），并采用动态权重多损失设计实现高效损失权重分配。轻量卷积块通过标准卷积与深度可分离卷积结合，在极低参数量下有效提取特征；分词移位块则融合空间移位块与深度可分离卷积，优化深层特征表示。各层动态调整损失权重，逼近最优解并增强训练稳定性。在UWMGI和MSD Colon数据集上的实验表明，LSU-Net显著优于多数现有先进分割架构。
原文摘要 · Abstract (English)
UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.
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