A geometric flow for segmenting vasculature in proton-density weighted MRI
A geometric flow for segmenting vasculature in proton-density weighted MRI
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DOI：
10.1016/j.media.2008.02.003
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发表时间：
2008-08-01
影响因子：
10.9
通讯作者：
Siddiqi, Kaleem
中科院分区：
文献类型：
--
作者：
Descoteaux, Maxime;Collins, D. Louis;Siddiqi, Kaleem
现代神经外科利用患者大脑解剖和血管系统的磁共振图像(MRI)在手术前进行规划，并在手术中进行指导。经常进行双回波采集，以产生质子密度(PD)和T2加权图像来评估肿瘤或病变附近的水肿。本文提出了一种新的分割PD图像中血管的几何方法，该方法同样适用于磁共振血管成像或增强磁共振成像的简单情况。从PD数据中获取血管系统具有临床意义，因为此类图像的获取很广泛，扫描过程是非侵入性的，血管分割方法的可用性可以消除在术前成像过程中额外的血管造影或基于对比的序列的需要。其关键思想是首先应用Frangi的血管度量[Frangi，A.，Niessen，W.，Vincken，K.L.，Viergever，M.A.，1998。多尺度血管增强滤波。见：医学图像计算和计算机辅助干预国际会议，计算机科学讲稿，第1496卷，第130-137页]以寻找假定的管状结构中心线及其估计的半径。然后，该测量被分发以创建矢量场，该矢量场允许Vasilevski和Siddiqi[Vasilevski，A.，Siddiqi，K.，2002]的流量最大化流动算法。流量最大化几何流动。模式分析和机器智能的IEEE会刊24(12)，1565-1578]将应用于恢复血管边界。我们对PD、MR血管成像和Gd增强MRI体积进行了定性验证，并提出了一种新的方法来在2D中使用掩蔽投影来可视化分割。我们在由PD、相位对比(PC)血管造影术和飞行时间(TOF)血管造影术体积组成的单一对象数据集上定量验证了该方法，并将TOF体积的专家分割版本视为基本事实。然后，我们在一个新的数字大脑模型的19个PD数据集上定量验证了该方法，其中半自动地从被视为基本事实的相应血管造影体中获得标记。一个重要的发现是，无论是单对象还是多对象研究，基本事实分割中90%或更多的血管系统都是从其他卷的自动分割中恢复的。(C)2008年，爱思唯尔出版。
Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient's cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton-density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. In this paper we develop a novel geometric flow for segmenting vasculature in PD images, which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. Obtaining vasculature from PD data is of clinical interest since the acquisition of such images is widespread, the scanning process is non-invasive, and the availability of vessel segmentation methods could obviate the need for an additional angiographic or contrast-based sequence during preoperative imaging. The key idea is to first apply Frangi's vesselness measure [Frangi, A., Niessen, W., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer Assisted Intervention, vol. 1496 of Lecture Notes in Computer Science, pp. 130-137] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which allows the flux maximizing flow algorithm of Vasilevskiy and Siddiqi [Vasilevskiy, A., Siddiqi, K., 2002. Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (12), 1565-1578] to be applied to recover vessel boundaries. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We validate the approach quantitatively on a single-subject data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, with an expert segmented version of the TOF volume viewed as the ground truth. We then validate the approach quantitatively on 19 PD data sets from a new digital brain phantom, with semi-automatically obtained labels from the corresponding angiography volumes viewed as ground truth. A significant finding is that both for the single-subject and multi-subject studies, 90% or more of the vasculature in the ground truth segmentation is recovered from the automatic segmentation of the other volumes. (C) 2008 Published by Elsevier B.V.