Recent advances in deep learning have greatly facilitated the automated segmentation of ultrasound images, which is essential for nodule morphological analysis. Nevertheless, most existing methods depend on extensive and precise annotations by domain experts, which are labor-intensive and time-consuming. In this study, we suggest using simple aspect ratio annotations directly from ultrasound clinical diagnoses for automated nodule segmentation. Especially, an asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels, i.e., conservative labels and radical labels, to train two asymmetric segmentation networks simultaneously. Subsequently, a conservative-radical-balance strategy (CRBS) strategy is proposed to complementally combine radical and conservative labels. An inconsistency-aware dynamically mixed pseudo-labels supervision (IDMPS) module is introduced to address the challenges of over-segmentation and under-segmentation caused by the two types of labels. To further leverage the spatial prior knowledge provided by clinical annotations, we also present a novel loss function namely the clinical anatomy prior loss. Extensive experiments on two clinically collected ultrasound datasets (thyroid and breast) demonstrate the superior performance of our proposed method, which can achieve comparable and even better performance than fully supervised methods using ground truth annotations.

翻译：近年来深度学习的进步极大促进了超声图像的自动分割，这对结节形态分析至关重要。然而，现有方法大多依赖领域专家提供的密集精确标注，耗时耗力。本研究提出直接利用超声临床诊断中的简单纵横比标注实现结节自动分割。具体而言，我们通过将纵横比标注扩展为保守标签和激进标签两种伪标签，构建非对称学习框架以同步训练两个非对称分割网络。随后提出保守-激进平衡策略（CRBS）对两种标签进行互补融合，并引入不一致感知动态混合伪标签监督（IDMPS）模块应对两类标签导致的过分割与欠分割问题。为充分利用临床标注提供的空间先验知识，我们创新性地设计临床解剖先验损失函数。在临床上采集的两个超声数据集（甲状腺与乳腺）上的大量实验表明，本方法性能优异，能够达到甚至超越使用真实标注的全监督方法。