标题： RARE-UNet：自适应医学图像分割的分辨率对齐路由条目 显示英文标题

标题： RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation

摘要： 准确的分割对于临床应用至关重要，但现有模型通常假设固定且高分辨率的输入，在面对现实场景中的低分辨率数据时性能会显著下降。 为解决这一限制，我们提出了RARE-UNet，这是一种分辨率感知的多尺度分割架构，能够动态适应输入的空间分辨率。 我们的设计核心包括在多个编码器深度集成的多尺度块、分辨率感知的路由机制以及一致性驱动的训练，该训练将多分辨率特征与全分辨率表示对齐。 我们在两个基准脑成像任务中评估了RARE-UNet，用于海马体和肿瘤分割。 与标准UNet、其多分辨率增强变体以及nnUNet相比，我们的模型在不同分辨率下平均Dice分数达到最高，分别为0.84和0.65，同时在低分辨率下保持一致的性能并显著减少推理时间。 这些结果突显了我们的架构在实现分辨率鲁棒分割方面的有效性和可扩展性。 代码可在以下地址获取：https://github.com/simonsejse/RARE-UNet. 显示英文摘要

摘要： Accurate segmentation is crucial for clinical applications, but existing models often assume fixed, high-resolution inputs and degrade significantly when faced with lower-resolution data in real-world scenarios. To address this limitation, we propose RARE-UNet, a resolution-aware multi-scale segmentation architecture that dynamically adapts its inference path to the spatial resolution of the input. Central to our design are multi-scale blocks integrated at multiple encoder depths, a resolution-aware routing mechanism, and consistency-driven training that aligns multi-resolution features with full-resolution representations. We evaluate RARE-UNet on two benchmark brain imaging tasks for hippocampus and tumor segmentation. Compared to standard UNet, its multi-resolution augmented variant, and nnUNet, our model achieves the highest average Dice scores of 0.84 and 0.65 across resolution, while maintaining consistent performance and significantly reduced inference time at lower resolutions. These results highlight the effectiveness and scalability of our architecture in achieving resolution-robust segmentation. The codes are available at: https://github.com/simonsejse/RARE-UNet.