论文标题
信仰功能在医学图像细分中的应用：评论
Application of belief functions to medical image segmentation: A review
论文作者
论文摘要
对不确定性的调查在关键风险应用中至关重要，例如医疗图像分割。信仰功能理论是不确定性分析的正式框架和多种证据融合，为医学图像分割做出了重大贡献，尤其是自从深度学习的发展以来。在本文中，我们使用信念函数理论对医学图像分割方法的主题进行了介绍。我们根据融合步骤对方法进行分类，并解释如何与信念功能理论建模并融合的信息。此外，我们讨论了基于信念功能的医学图像细分的挑战和局限性，并提出了未来研究的方向。未来的研究可以研究信仰功能理论和深度学习，以实现更有前途和可靠的细分结果。
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertainty analysis and multiple evidence fusion, has made significant contributions to medical image segmentation, especially since the development of deep learning. In this paper, we provide an introduction to the topic of medical image segmentation methods using belief function theory. We classify the methods according to the fusion step and explain how information with uncertainty or imprecision is modeled and fused with belief function theory. In addition, we discuss the challenges and limitations of present belief function-based medical image segmentation and propose orientations for future research. Future research could investigate both belief function theory and deep learning to achieve more promising and reliable segmentation results.
