Research on thyroid CT image segmentation based on U-shaped convolutional neural network

Computer-aided thyroid CT image segmentation aims to provide imaging physicians and clinicians with auxiliary diagnostic suggestions and improve the efficiency of physicians in diagnosing the thyroid region. However, it is still a challenging task to distinguish the thyroid from other surrounding tissues due to adhesions in thyroid CT images caused by thyroid disease. To achieve accurate segmentation of thyroid CT images under the intervention of different types of thyroid nodules, we proposed a thyroid segmentation network named ResUnet by introducing the residual learning idea to UNet. Our network controls the gradient dispersion by incorporating a batch normalization operation and an intermediate layer regularization operation, then solves the degradation problem by introducing the residual connections into the convolution operation. Moreover, our ResUnet network can converge faster with the same number of layers, thus supporting a deeper design of the network. Extensive experiments also validated the high accuracy (94.10%), specificity (98.94%), and sensitivity (96.34%) of the proposed ResUnet for the segmentation of thyroid nodules, which can assist CT physicians in the diagnosis of the thyroid gland.