Volume-based features for detection of bladder wall abnormal regions via MR cystography.
Volume-based features for detection of bladder wall abnormal regions via MR cystography.
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通过 MR 膀胱造影检测膀胱壁异常区域的基于体积的特征
DOI：
10.1109/tbme.2011.2158541
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发表时间：
2011-09
期刊：
影响因子：
--
通讯作者：
Liang Z
中科院分区：
文献类型：
--
作者：
Duan C;Yuan K;Liu F;Xiao P;Lv G;Liang Z
本文提出了一种通过磁共振膀胱造影检测膀胱壁可疑异常区域的框架。使用基于卷的特性。首先，将膀胱壁分成几层，在此基础上找到从内边界的每个体素到外边界的路径。利用路径长度测量壁厚，利用弯曲率（BR）项测量内边界体素的几何特性，确定了代表内边界异常的种子体素。然后，通过跟踪每个种子的路径，构建加权BR项来确定路径上和膀胱壁内的可疑体素。所有可疑体素被分组在一起作为异常区域。本工作与以往大多数计算机辅助膀胱肿瘤检测报道在两个方面有显著不同。首先，与计算机断层扫描图像相比，使用t1加权MR图像可以提供更好的膀胱壁图像对比度和纹理信息。其次，以往的报道大多是通过表面渲染的方法在重建的三维膀胱模型上检测异常并显示异常，而我们进一步确定了膀胱壁内可能出现异常的区域。本研究旨在建立一种无创的膀胱肿瘤检测和异常区域描绘方法，为进一步的临床分析，如肿瘤的侵袭深度和虚拟膀胱镜诊断提供可能。用5个数据集（2例患者和3例志愿者）对所提出的方法进行了测试，所有的肿瘤都被该方法检测到，并测量了计算机与专家划定的区域的重叠率。结果表明，该方法可以通过磁共振膀胱造影检测膀胱壁异常区域。
This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume-based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found. By using the path length to measure the wall thickness and a bent rate (BR) term to measure the geometry property of the voxels on the inner border, the seed voxels representing the abnormalities on the inner border are determined. Then, by tracing the path from each seed, a weighted BR term is constructed to determine the suspected voxels, which are on the path and inside the bladder wall. All the suspected voxels are grouped together for the abnormal region. This work is significantly different from most of the previous computer-aided bladder tumor detection reports on two aspects. First of all, the T1-weighted MR images are used which give better image contrast and texture information for the bladder wall, comparing with the computed tomography images. Second, while most previous reports detected the abnormalities and indicated them on the reconstructed 3-D bladder model by surface rendering, we further determine the possible region of the abnormality inside the bladder wall. This study aims at a noninvasive procedure for bladder tumor detection and abnormal region delineation, which has the potential for further clinical analysis such as the invasion depth of the tumor and virtual cystoscopy diagnosis. Five datasets including two patients and three volunteers were used to test the presented method, all the tumors were detected by the method, and the overlap rates of the regions delineated by the computer against the experts were measured. The results demonstrated the potential of the method for detecting bladder wall abnormal regions via MR cystography.