提出可自适应分辨率的医学图像分割模型，提升低分辨率下的精度与速度。
RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation
- 通过多尺度模块和动态路由机制，根据输入分辨率自动调整推理路径。
- 在脑部海马体和肿瘤分割任务中，平均Dice达0.84和0.65，且低分辨率下推理更快。
- 适合实际医疗场景中输入分辨率不固定的图像分割应用。
精准分割对临床应用至关重要，但现有模型通常假设输入为固定高分辨率，在真实场景中面对低分辨率数据时性能显著下降。为此，我们提出RARE-UNet，一种面向分辨率感知的多尺度分割架构，能动态适应输入的空间分辨率。其核心设计包括：在编码器多个深度集成多尺度块、分辨率感知路由机制，以及一致性驱动训练，使多分辨率特征与全分辨率表示对齐。我们在两个脑部影像基准任务（海马体和肿瘤分割）上评估RARE-UNet。相比标准UNet、其多分辨率增强变体及nnUNet，本模型在不同分辨率下均取得最高平均Dice分数（0.84和0.65），并在低分辨率下保持一致性能并显著降低推理时间。结果表明该架构在实现分辨率鲁棒分割方面具有高效性与可扩展性。代码已公开于：https://github.com/simonsejse/RARE-UNet。
原文摘要 · Abstract (English)
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.
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