RDTE-UNet：一种用于精确医学图像分割的边界与细节感知UNet
RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentation
AI总结：
针对医学图像解剖变异性与边界模糊性导致精细结构分割困难的问题，提出RDTE-UNet分割网络，通过混合Transformer主干及ASBE、HVDA和EulerFF三个模块增强边界与细节特征，在Synapse和BUSI数据集上取得了优越的分割精度与边界质量。
摘要：
医学图像分割对于计算机辅助诊断和治疗规划至关重要，但显著的解剖变异性和边界模糊性阻碍了对精细结构的可靠勾画。我们提出了RDTE-UNet，这是一种将局部建模与全局上下文相统一的分割网络，旨在增强边界勾画和细节保留。RDTE-UNet采用混合ResBlock细节感知Transformer主干和三个模块：用于自适应边界增强的ASBE、用于细粒度特征建模的HVDA，以及由欧拉公式引导进行融合加权的EulerFF。这些组件共同提升了在形态、方向和尺度上的结构一致性与边界准确性。在Synapse和BUSI数据集上，RDTE-UNet在分割精度和边界质量方面达到了可比的水平。
英文摘要：
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.
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。
