Medical image segmentation plays a pivotal role in automated diagnostic and treatment planning systems. In this work, we present DAUNet, a novel lightweight UNet variant that integrates Deformable V2 Convolutions and Parameter-Free Attention (SimAM) to improve spatial adaptability and context-aware feature fusion without increasing model complexity. DAUNet's bottleneck employs dynamic deformable kernels to handle geometric variations, while the decoder and skip pathways are enhanced using SimAM attention modules for saliency-aware refinement. Extensive evaluations on two challenging datasets, FH-PS-AoP (fetal head and pubic symphysis ultrasound) and FUMPE (CT-based pulmonary embolism detection), demonstrate that DAUNet outperforms state-of-the-art models in Dice score, HD95, and ASD, while maintaining superior parameter efficiency. Ablation studies highlight the individual contributions of deformable convolutions and SimAM attention. DAUNet's robustness to missing context and low-contrast regions establishes its suitability for deployment in real-time and resource-constrained clinical environments.

翻译：医学图像分割在自动化诊断与治疗规划系统中发挥着关键作用。本文提出DAUNet，一种新颖的轻量级UNet变体，该模型集成了可变形V2卷积与无参数注意力机制（SimAM），在不增加模型复杂度的前提下提升了空间适应性与上下文感知特征融合能力。DAUNet的瓶颈层采用动态可变形卷积核以处理几何形变，解码器与跳跃连接路径则通过SimAM注意力模块进行显著性感知的特征优化。在FH-PS-AoP（胎儿头围与耻骨联合超声）和FUMPE（基于CT的肺栓塞检测）两个具有挑战性的数据集上进行广泛评估，结果表明DAUNet在Dice系数、HD95和ASD指标上均优于现有先进模型，同时保持了卓越的参数效率。消融实验验证了可变形卷积与SimAM注意力模块各自的贡献。DAUNet对缺失上下文与低对比度区域的鲁棒性，证明了其适用于实时性与资源受限的临床部署环境。