LHU-Net：一种用于高性价比、高性能体积分割的精简混合 U-Net
LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation
AI总结：
提出LHU-Net精简混合U-Net，通过优先提取空间特征再优化通道特征，在四个基准数据集上以更少参数和计算量实现了最先进的医学图像体积分割性能。
摘要：
Transformer架构的兴起推动了医学图像分割的发展，催生了结合卷积神经网络（CNN）与Transformer的混合模型。然而，这些模型通常面临过度复杂的问题，且未能有效整合对精确分割至关重要的空间特征与通道特征。为解决此问题，我们提出了LHU-Net，一种用于体积医学图像分割的精简混合U-Net。LHU-Net优先提取空间特征，随后优化通道特征，兼顾了效率与准确性。在四个基准数据集（Synapse、Left Atrial、BraTS-Decathlon和Lung-Decathlon）上进行评估，LHU-Net在多种模态（CT/MRI）和输出配置下均持续优于现有模型。它实现了最先进的Dice分数，同时参数量比竞争模型少四倍，FLOPs减少20%，且无需预训练、额外数据或模型集成。LHU-Net平均拥有1100万参数，为计算效率和分割精度设定了新基准。我们的实现已在GitHub上公开：https://github.com/xmindflow/LHUNet
英文摘要：
The rise of Transformer architectures has advanced medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers. However, these models often suffer from excessive complexity and fail to effectively integrate spatial and channel features, crucial for precise segmentation. To address this, we propose LHU-Net, a Lean Hybrid U-Net for volumetric medical image segmentation. LHU-Net prioritizes spatial feature extraction before refining channel features, optimizing both efficiency and accuracy. Evaluated on four benchmark datasets (Synapse, Left Atrial, BraTS-Decathlon, and Lung-Decathlon), LHU-Net consistently outperforms existing models across diverse modalities (CT/MRI) and output configurations. It achieves state-of-the-art Dice scores while using four times fewer parameters and 20% fewer FLOPs than competing models, without the need for pre-training, additional data, or model ensembles. With an average of 11 million parameters, LHU-Net sets a new benchmark for computational efficiency and segmentation accuracy. Our implementation is available on GitHub: https://github.com/xmindflow/LHUNet
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
