Recognition of thyroid glands and tissues of the neck is vital for screening related diseases in ultrasound videos. This task is subjective, challenging, and dependent on the experience of sonographer in current clinical practice. The purpose is to develop a fully automated thyroid gland and tissues of neck recognition framework to assist doctors in distinguishing the boundaries of different tissues. In this paper, we propose a novel deep learning framework that consists of a feature extraction network, region proposal network, object detection head, and spatial pyramid RoIAlign-based segmentation head. Designed spatial pyramid RoIAlign can efficiently capture local and global context features, and aggregates the multiple context information that makes the result much more reliable. A large dataset is constructed to train the proposed method. The performance is evaluated using the COCO metrics. The experimental results demonstrate that the proposed deep learning method can effectively realize the automatic recognition of the thyroid gland and tissues of neck in ultrasound videos. Considering the clinical practical application scenarios, we developed an automatic recognition system of thyroid and neck tissue based on edge computing, which can expediently assist doctors in distinguishing the boundaries between different tissues.

中文翻译：

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用于自动识别超声图像中甲状腺和颈部组织的新型深度学习框架


甲状腺和颈部组织的识别对于超声视频中筛查相关疾病至关重要。这项任务是主观的、具有挑战性的，并且取决于超声检查医师在当前临床实践中的经验。目的是开发一个全自动的甲状腺和颈部组织识别框架，以帮助医生区分不同组织的边界。在本文中，我们提出了一种新颖的深度学习框架，该框架由特征提取网络、区域提议网络、对象检测头和基于空间金字塔 RoIAlign 的分割头组成。设计的空间金字塔 RoIAlign 可以有效捕获局部和全局上下文特征，并聚合多个上下文信息，使结果更加可靠。构建一个大型数据集来训练所提出的方法。使用 COCO 指标评估性能。实验结果表明，所提出的深度学习方法可以有效实现超声视频中甲状腺和颈部组织的自动识别。结合临床实际应用场景，我们开发了基于边缘计算的甲状腺和颈部组织自动识别系统，可以方便地辅助医生区分不同组织之间的边界。