A real-time architecture of medical image semantic segmentation called Fully Convolution dense Dilated Network, is proposed to improve the segmentation efficiency while ensuring high accuracy. Considering low resolution and contrast, interferences of shadows, as well as differences in nodules’ position and size, accurate ultrasound images’ segmentation cannot be obtained easily. Therefore, a novel layer that integrates the advantages of dense connectivity, dilated convolutions and factorized filters, is proposed in an attempt to remain efficient while retaining remarkable accuracy. Dense connectivity combines low-level fine segmentation with high-level coarse segmentation to extract more features from ultrasound images. Dilated convolution can expand the receptive field of the filter, and the problem of differences in nodules’ size and position can be solved with different sizes of filters. This study also introduces factorized filters into the network to further optimize the efficiency of the model. In addition, aiming at the class imbalance problem in medical image semantic segmentation, a loss function optimization method is proposed which further improves the accuracy of the network. A thorough set of experiments based on thyroid dataset show that the proposed model achieves state-of-the-art performance in terms of robustness and efficiency.

提出了一种医学图像语义分割的实时架构，称为全卷积密集扩张网络，以提高分割效率，同时确保准确性。考虑到低分辨率和对比度，阴影的干扰以及结节位置和大小的差异，无法轻松获得准确的超声图像分割。因此，提出了一种新颖的层，该层结合了密集连通性，膨胀卷积和因式分解滤波器的优点，以试图在保持出色准确性的同时保持效率。密集连接将低级精细分割与高级粗略分割相结合，以从超声图像中提取更多特征。扩张的卷积可以扩展滤波器的接收场，用不同尺寸的过滤器可以解决结节大小和位置不同的问题。这项研究还将分解滤波器引入网络，以进一步优化模型的效率。针对医学图像语义分割中的类不平衡问题，提出一种损失函数优化方法，进一步提高了网络的准确性。基于甲状腺数据集的一组详尽的实验表明，该模型在鲁棒性和效率方面达到了最先进的性能。提出了一种损失函数优化方法，可以进一步提高网络的精度。基于甲状腺数据集的一组详尽的实验表明，该模型在鲁棒性和效率方面达到了最先进的性能。提出了一种损失函数优化方法，可以进一步提高网络的精度。一组基于甲状腺数据集的全面实验表明，该模型在鲁棒性和效率方面达到了最先进的性能。