Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.

ABSTRACT: 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 segmentatio

SUBMITTER: Meng Q 

PROVIDER: S-EPMC6892638 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature