An adaptive window-setting scheme for segmentation of bladder tumor surface via MR cystography.
An adaptive window-setting scheme for segmentation of bladder tumor surface via MR cystography.
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DOI：
10.1109/titb.2012.2200496
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发表时间：
2012-07
期刊：
影响因子：
--
通讯作者：
Liang Z
中科院分区：
文献类型：
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作者：
Duan C;Yuan K;Liu F;Xiao P;Lv G;Liang Z
本文提出了一种自适应窗口设置方案，用于T1加权磁共振（MR）图像中膀胱肿瘤表面的无创检测和分割。首先在膀胱壁的内边缘覆盖一组半径不同的球形探测窗。通过提取候选肿瘤窗口并排除假阳性（FP）候选，整个膀胱肿瘤表面被检测到，并通过剩余的窗口分割。与以往的膀胱肿瘤检测方法主要关注肿瘤的存在不同，本文除了检测肿瘤的存在外，还强调对整个肿瘤表面进行分割。10个临床T1加权MR图像数据集（5名志愿者和5名患者）验证了所提出的方案。10个数据集中的膀胱肿瘤表面和正常膀胱壁内边界分别被223个和10491个窗口覆盖。如此大数量的检测窗口使得验证在统计上有意义。在FP约简步骤中，通过使用受试者操作特征或ROC分析来获得最佳特征组合。验证结果表明，该方案在分割整个肿瘤表面具有高灵敏度和低FP率的潜力。这项工作继承了我们以前的膀胱壁自动分割的结果，将是一个重要的元素，在我们的MR为基础的虚拟膀胱镜或MR膀胱造影系统。
This paper proposes an adaptive window-setting scheme for non-invasive detection and segmentation of bladder tumor surface in T1-weighted magnetic resonance (MR) images. The inner border of the bladder wall is firstly covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows. Different from previous bladder tumor detection methods which are mostly focusing on the existence of a tumor, this paper emphasizes segmenting the entire tumor surface in addition to detecting the presence of the tumor. The presented scheme was validated by 10 clinical T1-weighted MR image datasets (5 volunteers and 5 patients). The bladder tumor surfaces and the normal bladder wall inner borders in the ten datasets were covered by 223 and 10491 windows, respectively. Such large number of the detecting windows makes the validation statistically meaningful. In the FP reduction step, the best feature combination was obtained by using receiver operating characteristics or ROC analysis. The validation results demonstrated the potential of this presented scheme in segmenting the entire tumor surface with high sensitivity and low FP rate. This work inherits our previous results of automatic segmentation of the bladder wall and will be an important element in our MR-based virtual cystoscopy or MR cystography system.