Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-scale Dense Networks

Abstract

The determination and interpretation of fetal standard planes (FSPs) in ultrasound examinations are the precondition and essential step for prenatal ultrasonography diagnosis. However, identifying multiple standard planes from ultrasound videos is a time-consuming and tedious task since there are only little differences between standard and non-standard planes in the adjacent scan frames. To address this challenge, we propose a general and efficient framework to detect several standard planes from ultrasound scan images or videos automatically. Specifically, a multi-scale dense networks (MSDNet) utilizing the multi-scale architecture and dense connection is exploited, which combines the fine level features from the shallow layers and coarse level features from the deep layers. Moreover, this MSDNet is resource efficient, and the cascade structure can adaptively select lightweight networks when test images are not complicated or computational resources limited. Experimental results based on our self-collected dataset demonstrate that the proposed method achieves a mean average precision (mAP) of 98.15% with half resources and double speeds in FSPs recognition task.