图像分割的深度学习:基于U_Net的甲状腺结节超声图像分割

Xueting Zhou, Yan Chen, Shoushan Liu
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引用次数: 1

摘要

本文的目的是探讨深度学习算法在甲状腺结节超声图像中的应用价值。利用MICCAI 2020挑战赛提供的7288张甲状腺结节超声图像数据集,基于U_Net框架,结合多尺度输入机制和改进的损失优化函数,通过持续训练找到最优模型,使计算机能够自主分割甲状腺结节。分割精度达到0.955,网络具有良好的分割性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning for image segmentation: Ultrasound image segmentation of thyroid nodules based on U_Net
The purpose of this article is to investigate the value of deep learning algorithms in the application of ultrasound images of thyroid nodules. Using a dataset of 7288 ultrasound images of thyroid nodules provided by the MICCAI 2020 Challenge, based on the U_Net framework, incorporating a multiscale input mechanism and improving loss optimization function, through continuous training to find the optimal model, so that the computer can autonomously segment the thyroid nodules. The segmentation accuracy reaches 0.955, and the network has good segmentation performance.
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