论文标题
基于学习的船舶跟踪算法：评论
Learning-Based Algorithms for Vessel Tracking: A Review
论文作者
论文摘要
开发有效的血管跟踪算法对于基于成像的诊断和血管疾病的治疗至关重要。血管跟踪旨在解决识别问题，例如关键（种子）检测，中心线提取和血管分割。已经开发了广泛的图像处理技术来克服主要归因于血管的复杂形态和血管造影的图像特征的复杂形态。本文介绍了有关血管跟踪方法的文献综述，重点是基于机器学习的方法。首先，审查了常规的基于机器学习的算法，然后提供了对基于深度学习的框架的一般调查。根据审查的方法，引入了评估问题。本文以讨论其余的紧迫性和未来的研究进行了讨论。
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.
