A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images

分割 人工智能 深度学习 计算机科学 卷积神经网络 计算机视觉 肌腱 模式识别(心理学) 人工神经网络 跟踪(教育) 解剖 医学 心理学 教育学
作者
Guangquan Zhou,En-Ze Huo,Mei Yuan,Ping Zhou,Ruoli Wang,Kai‐Ni Wang,Yang Chen,Xiaopu He
出处
期刊:IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control [Institute of Electrical and Electronics Engineers]
卷期号:67 (12): 2531-2542 被引量:17
标识
DOI:10.1109/tuffc.2020.2979481
摘要

Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for understanding the mechanics and pathological conditions of the muscle-tendon unit. However, the lack of reliable and efficient identification of MTJ due to poor image quality and boundary ambiguity restricts its application in motion analysis. In recent years, with the rapid development of deep learning, the region-based convolution neural network (RCNN) has shown great potential in the field of simultaneous objection detection and instance segmentation in medical images. This article proposes a region-adaptive network (RAN) to localize MTJ region and to segment it in a single shot. Our model learns about the salient information of MTJ with the help of a composite architecture. Herein, a region-based multitask learning network explores the region containing MTJ, while a parallel end-to-end U-shaped path extracts the MTJ structure from the adaptively selected region for combating data imbalance and boundary ambiguity. By demonstrating the ultrasound images of the gastrocnemius, we showed that the RAN achieves superior segmentation performance when compared with the state-of-the-art Mask RCNN method with an average Dice score of 80.1%. Our proposed method is robust and reliable for advanced muscle and tendon function examinations obtained by ultrasound imaging.
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