关键词: 图像分割, 甲状腺结节, 混合膨胀卷积, 注意力机制, U-Net++

Abstract: Thyroid disease is one of the most frequently diagnosed nodular lesions in adult population， and it’s incidence is increasing year by year. With the development of artificial intelligence technology， the automatic diagnosis of thyroid ultrasound images using computer vision technology can significantly improve the accuracy and efficiency of diagnosis. However， most image segmentation methods based on deep learning， limited by the size of receptive field， cannot focus on the important features of the image in time and extract them effectively， resulting in low segmentation accuracy. In order to solve the above problems， a new deep learning network model HA-UNet ++ （Hybrid Dilated Convolution-Attention-UNet++） is adopted in this paper to segment ultrasonic images of thyroid nodules. HA-UNet ++ improves backbone network structure at each stage of encoding path. At the same time， hybrid dilated convolution is added to the convolution blocks with three layers of convolution in the network， and attention mechanism is added to each convolution block， so that it can quickly predict the enhanced thyroid nodule data set. On this basis， thyroid nodules are labeled and segmented.

Key words:  , image segmentation, thyroid nodules, hybrid dilated convolution, attention mechanism, U-Net++