近年来,基于稀疏标注的涂鸦式弱监督学习方法,在医学影像分割中展现出了以较低标注成本而达到良好分割效果的潜力。但弱监督信号有限,模型极易出现侧重目标主体而忽视目标边缘的问题,进一步导致了较大的边界分割误差。为解决该问题,本研究提出Weak-Spatially-Agile-Mamba-UNet（WSAM-UNet）模型,利用卷积神经U型子模型进行局部特征提取、使用动态自适应注意力U型子模型进行相关像素动态感知,以及通过视觉曼巴U型子模型进行全局上下文建模。最终,经过在ACDC数据集上的实验验证,WSAM-UNet生成的分割掩模与真实标签在重叠率上达到0.893 19,在豪斯多夫距离误差和平均表面距离误差指标上分别降低至4.186 5和1.035 2,与其他多个基线模型相比,该模型在整体分割精度和边界误差控制方面达到了最优平衡。
Abstract
Recently, scribble-based weakly-supervised learning methods have shown great potential in medical image segmentation, achieving promising results at a low annotation cost. However, the limited supervisory signals often lead models to overemphasize the main body of the target while overlooking its precise boundaries, consequently resulting in significant segmentation errors along the edges. To tackle this issue, we propose a Weak-Spatially-Agile-Mamba-UNet (WSAM-UNet) model. This architecture integrates a convolutional U-Net sub-model for local feature extraction, a dynamic adaptive attention U-Net sub-model for context-aware pixel perception, and a Visual Mamba U-Net sub-model for global contextual modeling. Extensive experiments on the ACDC dataset demonstrate that our WSAM-UNet achieves an overlap rate of 0.893 19 with the ground truth, while reducing the Hausdorff Distance and Average Surface Distance to 4.186 5 and 1.035 2, respectively. Compared to several strong baselines, WSAM-UNet achieves an optimal balance between overall segmentation accuracy and boundary error control.
关键词
医学图像分割 /
弱监督学习 /
稀疏注解 /
多模态信息融合 /
涂鸦监督
Key words
medical image segmentation /
weakly supervised learning /
sparse annotation /
multimodal information fusion /
scribble supervision
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