Segmenting anatomical structures and lesions from ultrasound images contributes to disease assessment. Weakly supervised learning (WSL) based on sparse annotation has achieved encouraging performance and demonstrated the potential to reduce annotation costs. This study attempts to introduce scribble-based WSL into ultrasound image segmentation tasks. However, ultrasound images often suffer from poor contrast and unclear edges, coupled with insufficient supervison signals for edges, posing challenges to edge prediction. Uncertainty modeling has been proven to facilitate models in dealing with these issues. Nevertheless, existing uncertainty estimation paradigms are not robust enough and often filter out predictions near decision boundaries, resulting in unstable edge predictions. Therefore, we propose leveraging predictions near decision boundaries effectively. Specifically, we introduce Dempster-Shafer Theory (DST) of evidence to design an Evidence-Guided Consistency strategy. This strategy utilizes high-evidence predictions, which are more likely to occur near high-density regions, to guide the optimization of low-evidence predictions that may appear near decision boundaries. Furthermore, the diverse sizes and locations of lesions in ultrasound images pose a challenge for CNNs with local receptive fields, as they struggle to model global information. Therefore, we introduce Visual Mamba based on structured state space sequence models, which achieves long-range dependency with linear computational complexity, and we construct a novel hybrid CNN-Mamba framework. During training, the collaboration between the CNN branch and the Mamba branch in the proposed framework draws inspiration from each other based on the EGC strategy. Experiments demonstrate the competitiveness of the proposed method. Dataset and code will be available on https://github.com/GtLinyer/MambaEviScrib.

翻译：从超声图像中分割解剖结构与病灶有助于疾病评估。基于稀疏标注的弱监督学习已取得令人鼓舞的性能，并展现出降低标注成本的潜力。本研究尝试将基于涂鸦的弱监督学习引入超声图像分割任务。然而，超声图像常存在对比度差、边缘模糊的问题，加之边缘监督信号不足，对边缘预测提出了挑战。不确定性建模已被证明有助于模型处理此类问题。然而，现有的不确定性估计范式鲁棒性不足，且常滤除决策边界附近的预测，导致边缘预测不稳定。因此，我们提出有效利用决策边界附近的预测。具体而言，我们引入证据理论来设计一种证据引导一致性策略。该策略利用更可能出现在高密度区域附近的高证据预测，来指导可能出现在决策边界附近的低证据预测的优化。此外，超声图像中病灶尺寸与位置的多样性对具有局部感受野的CNN构成了挑战，因其难以建模全局信息。因此，我们引入基于结构化状态空间序列模型的Visual Mamba，其能以线性计算复杂度实现长程依赖，并构建了一种新颖的混合CNN-Mamba框架。在训练过程中，所提框架中CNN分支与Mamba分支的协作基于证据引导一致性策略相互借鉴。实验证明了所提方法的竞争力。数据集与代码将在 https://github.com/GtLinyer/MambaEviScrib 上公开。