Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review.
Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review.
复制标题
医学影像中肝脏血管分割的计算方法综述。
DOI：
10.3390/s21062027
复制
发表时间：
2021-03-12
期刊：
影响因子：
--
通讯作者：
Kassjański M
中科院分区：
文献类型：
--
作者：
Ciecholewski M;Kassjański M
肝脏血管的分割非常重要，因为它对于制定诊断，规划和提供治疗以及评估临床程序的结果至关重要。不同的成像技术可用于临床实践，因此分割方法应考虑成像技术的特点。基于文献，本文综述了最先进和有效的肝脏血管分割方法，以及根据所使用的指标的性能。本文包括四种成像方法的结果，即：计算机断层扫描（CT），计算机断层扫描血管造影（CTA），磁共振（MR）和超声（USG）。还介绍了研究中使用的公开数据集。本文可以帮助研究人员更好地了解现有的材料和方法，从而更容易开发新的，更有效的解决方案，以及改进现有的方法。本文详细分析了各种分割方法，这些方法可以分为三组：活动轮廓、基于跟踪的和机器学习技术。对于每一组的方法，他们的理论和实践特点进行了讨论，并强调的优点和缺点。最先进的和有前途的方法也提出了建议。然而，我们得出结论，肝脏血管分割仍然是一个悬而未决的问题，因为研究人员需要解决的各种缺陷和限制，并试图消除所使用的解决方案。
The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used.
登录
查看更多内容
影响因子：
4.6
作者：
Esneault, Simon;Lafon, Cyril;Dillenseger, Jean-Louis
通讯作者：
Dillenseger, Jean-Louis
影响因子：
32.8
作者：
Bengio, Yoshua
通讯作者：
Bengio, Yoshua
影响因子：
10.6
作者：
Gass, Tobias;Szekely, Gabor;Goksel, Orcun
通讯作者：
Goksel, Orcun
影响因子：
8
作者：
Bradley, AP
通讯作者：
Bradley, AP
DOI：
10.1109/tip.2012.2186306
发表时间：
2012-04
期刊：
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
影响因子：
--
作者：
Chen X;Udupa JK;Bagci U;Zhuge Y;Yao J
通讯作者：
Yao J