Ultrasonic thyroid nodule detection method based on U-Net network. (February 2021) - Chu, Chen; Zheng, Jihui; Zhou, Yong
Highlights: U-Net-based thyroid nodule detection is proposed to perform computed aided diagnosis. An interactive segmentation method guided by markers is implemented. Our algorithm compares segmentation accuracy with VGG19, Inception V3, and DenseNet 161. The U-Net segmentation gives results that are closer to the manually defined nodules. Compared with the other networks, the U-Net network has the fastest computing speed. Abstract: Objective: Aiming at the time consuming processing of existing thyroid nodule detection and difficulty in feature extraction, U-Net-based thyroid nodule detection is proposed to perform computed aided diagnosis. Method: This paper proposes a mark-guided ultrasound deep network segmentation model of thyroid nodules. By comparing with VGG19, Inception V3, DenseNet 161, segmentation accuracy, segmentation edge and network operation time, it is found that the algorithm in this paper has relative advantages. Results: U-Net network-based ultrasound thyroid nodules segmented the nodule area overlapped with the manually depicted nodule area close to 100%, the segmentation accuracy rate was as high as 0.9785, and the U-Net segmentation result was closer to the manually depicted nodule. The accuracy of U-Net segmentation of the thyroid is about 3% higher than the other three networks. Conclusion: The segmentation of nodules based on U-Net proposed in this paper significantly improves the segmentation accuracy of thyroid nodules with a small training data set, and provides a comprehensive reference for clinical diagnosis and treatment.