A Novel Deep Learning Framework for Automatic Recognition of Thyroid Gland and Tissues of Neck in Ultrasound Image

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.