  Detecting acoustic shadows in ultrasound images is important in many clinical
and engineering applications. Real-time feedback of acoustic shadows can guide
sonographers to a standardized diagnostic viewing plane with minimal artifacts
and can provide additional information for other automatic image analysis
algorithms. However, automatically detecting shadow regions using
learning-based algorithms is challenging because pixel-wise ground truth
annotation of acoustic shadows is subjective and time consuming. In this paper
we propose a weakly supervised method for automatic confidence estimation of
acoustic shadow regions. Our method is able to generate a dense shadow-focused
confidence map. In our method, a shadow-seg module is built to learn general
shadow features for shadow segmentation, based on global image-level
annotations as well as a small number of coarse pixel-wise shadow annotations.
A transfer function is introduced to extend the obtained binary shadow
segmentation to a reference confidence map. Additionally, a confidence
estimation network is proposed to learn the mapping between input images and
the reference confidence maps. This network is able to predict shadow
confidence maps directly from input images during inference. We use evaluation
metrics such as DICE, inter-class correlation and etc. to verify the
effectiveness of our method. Our method is more consistent than human
annotation, and outperforms the state-of-the-art quantitatively in shadow
segmentation and qualitatively in confidence estimation of shadow regions. We
further demonstrate the applicability of our method by integrating shadow
confidence maps into tasks such as ultrasound image classification, multi-view
image fusion and automated biometric measurements.
