  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.
