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.

本研究提出了一种基于简单的长宽比批注的自动结节分割方法，并通过扩展长宽比批注标签，采用两种类型的伪标签训练两个不对称分割网络，结合保守和激进标签采用一种平衡策略，同时引入了一种动态混合伪标签监督模块以解决过分割和欠分割问题。此外，为了进一步利用临床批注提供的空间先验知识，本研究还提出了一种新的损失函数，即临床解剖先验损失。在甲状腺和乳房的两个临床采集的超声数据集上的广泛实验表明，我们提出的方法的性能优于其他方法，甚至可以实现与使用真实批注的全监督方法相当甚至更好的性能。