A coupled level set framework for bladder wall segmentation with application to MR cystography.
A coupled level set framework for bladder wall segmentation with application to MR cystography.
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DOI：
10.1109/tmi.2009.2039756
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发表时间：
2010-03
影响因子：
10.6
通讯作者：
Lu H
中科院分区：
文献类型：
--
作者：
Duan C;Liang Z;Bao S;Zhu H;Wang S;Zhang G;Chen JJ;Lu H
本文提出了一种基于T1加权磁共振(MR)图像的膀胱壁分割的耦合水平集(LS)框架，并将其应用于仿真膀胱镜(即MR膀胱造影术)中。该框架使用两个协作LS函数和区域自适应聚类算法来描绘膀胱壁，以用于逐体素的壁厚测量。它在四个方面与大多数已有的膀胱分割工作有很大的不同。首先，虽然大多数以前的工作只分割墙的内边界或至多手动分割外边界，但我们的框架同时自动提取内边界和外边界，除非初始种子点是由人工选择的。其次，它适用于尿液中信号降低的T1加权图像，而不是T2加权方案和计算机断层扫描中的增强信号。第三，通过考虑图像的全局亮度分布和局部亮度对比度，该框架中定义的图像能量函数对非均匀效应、运动伪影和图像噪声具有更强的免疫力。最后，通过模拟两等势面之间的电场线的两个边界之间的积分路径长度来测量膀胱壁厚度。该框架在六个数据集上进行了测试，并与著名的Chan-Vese(C-V)LS模型进行了比较。五位专家盲目地对所提出的框架和C-V模型的内外边界进行了分割。这些分数在统计学上显示出在检测内、外边界方面的进步。
In this paper, we propose a coupled level set (LS) framework for segmentation of bladder wall using T1-weighted magnetic resonance (MR) images with clinical applications to virtual cystoscopy (i.e., MR cystography). The framework uses two collaborative LS functions and a regional adaptive clustering algorithm to delineate the bladder wall for the wall thickness measurement on a voxel-by-voxel basis. It is significantly different from most of the pre-existing bladder segmentation work in four aspects. First of all, while most previous work only segments the inner border of the wall or at most manually segments the outer border, our framework extracts both the inner and outer borders automatically except that the initial seed point is given by manual selection. Secondly, it is adaptive to T1-weighted images with decreased intensities in urine, as opposed to enhanced intensities in T2-weighted scenario and computed tomography. Thirdly, by considering the image global intensity distribution and local intensity contrast, the defined image energy function in the framework is more immune to inhomogeneity effect, motion artifacts and image noise. Finally, the bladder wall thickness is measured by the length of integral path between the two borders which mimic the electric field line between two iso-potential surfaces. The framework was tested on six datasets with comparison to the well-known Chan-Vese (C-V) LS model. Five experts blindly scored the segmented inner and outer borders of the presented framework and the C-V model. The scores demonstrated statistically the improvement in detecting the inner and outer borders.