A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images
A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images
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用于肌肉超声图像中肌腱连接分割的单次区域自适应网络
DOI：
10.1109/tuffc.2020.2979481
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Xiao-Pu He
中科院分区：
文献类型：
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作者：
Zhou Guang-Quan;Huo En-Ze;Yuan Mei;Zhou Ping;Wang Ruo-Li;Wang Kai-Ni;Chen Yang;Xiao-Pu He
在连续的超声图像中跟踪肌腱接头（MTJ）对于理解肌肉-肌腱单元的力学和病理状况至关重要。然而，由于图像质量差和边界模糊，MTJ缺乏可靠和有效的识别，限制了其在运动分析中的应用。近年来，随着深度学习的快速发展，基于区域的卷积神经网络（RCNN）在医学图像中的同时目标检测和实例分割领域显示出巨大的潜力。本文提出了一种区域自适应网络（RAN）定位MTJ区域，并在一个单一的镜头分割它。我们的模型在复合架构的帮助下学习MTJ的显著信息。在此，基于区域的多任务学习网络探索包含MTJ的区域，而并行的端到端U形路径从自适应选择的区域提取MTJ结构以对抗数据不平衡和边界模糊。通过演示腓肠肌的超声图像，我们表明，与最先进的Mask RCNN方法相比，RAN实现了上级分割性能，平均Dice得分为80.1%。我们提出的方法是强大的和可靠的先进的肌肉和肌腱功能检查超声成像。
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.