论文标题
医学超声图片细分中的深度学习：评论
Deep Learning in Medical Ultrasound Image Segmentation: a Review
论文作者
论文摘要
将机器学习技术（尤其是深度学习）应用于医学图像细分中，由于其最先进的性能和结果。这可能是为临床诊断提供可靠基础的关键步骤，例如人体组织的3D重建，图像引导的干预措施，图像分析和可视化。在这篇评论文章中，基于深度学习的超声图像细分方法首先根据其体系结构和培训将六个主要组归为六个主要组。其次，对于每个组，选择，介绍，分析和总结了几种当前代表性算法。此外，总结了图像分割和超声图分割数据集的常见评估方法。此外，审查了当前方法及其评估的性能。最后，讨论了医学超声图像细分的挑战和潜在研究方向。
Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventions, image analyzing and visualization. In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first. Secondly, for each group, several current representative algorithms are selected, introduced, analyzed and summarized in detail. In addition, common evaluation methods for image segmentation and ultrasound image segmentation datasets are summarized. Further, the performance of the current methods and their evaluations are reviewed. In the end, the challenges and potential research directions for medical ultrasound image segmentation are discussed.
