提升医学图像分割边界精度，专注细节与结构一致性。
RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentation
- 融合局部建模与全局上下文，增强边界和细节表征。
- 在Synapse和BUSI数据集上实现高精度边界分割。
- 适合需要精细解剖结构分割的临床诊断场景。
医学图像分割对辅助诊断和治疗规划至关重要，但解剖结构变异大、边界模糊仍阻碍精细结构的可靠分割。本文提出RDTE-UNet，通过统一局部建模与全局上下文，强化边界清晰度与细节保留能力。该网络采用混合残差块细节感知变换器骨干，并引入三个模块：自适应边界增强（ASBE）、细粒度特征建模（HVDA）以及基于欧拉公式引导的融合加权（EulerFF），共同提升形态、方向与尺度下的结构一致性与边界准确率。在Synapse和BUSI数据集上，其分割精度与边界质量达到可比水平。
原文摘要 · Abstract (English)
Medical image segmentation is essential for computer-assisted diagnosis and treatment planning, yet substantial anatomical variability and boundary ambiguity hinder reliable delineation of fine structures. We propose RDTE-UNet, a segmentation network that unifies local modeling with global context to strengthen boundary delineation and detail preservation. RDTE-UNet employs a hybrid ResBlock detail-aware Transformer backbone and three modules: ASBE for adaptive boundary enhancement, HVDA for fine-grained feature modeling, and EulerFF for fusion weighting guided by Euler's formula. Together, these components improve structural consistency and boundary accuracy across morphology, orientation, and scale. On Synapse and BUSI dataset, RDTE-UNet has achieved a comparable level in terms of segmentation accuracy and boundary quality.
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