Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images.
Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images.
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基于随机游走的 3D 经直肠超声图像前列腺分割。
DOI：
10.1117/12.2216526
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Fei,Baowei
中科院分区：
文献类型：
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作者：
Ma,Ling;Guo,Rongrong;Tian,Zhiqiang;Venkataraman,Rajesh;Sarkar,Saradwata;Liu,Xiabi;Nieh,PeterT;Master,VirajV;Schuster,DavidM;Fei,Baowei
本文提出了一种新的半自动分割方法的前列腺三维经直肠超声图像（TRUS）相结合的区域和分类信息。我们使用一种随机游走算法来有效和灵活地表达区域信息，因为它可以避免分割泄漏和收缩偏差。我们进一步使用决策树作为分类器，以区分前列腺和非前列腺组织，因为它的速度快，上级的性能，特别是对于一个二进制分类问题。我们的分割算法是初始化与用户粗略标记的前列腺和非前列腺点上的中腺切片被拟合成一个椭圆，以获得更多的点。基于这些拟合的种子点，我们运行随机游走算法来分割中腺切片上的前列腺。将分割的轮廓和来自决策树分类的信息相结合，以确定其他切片的初始种子点。然后使用随机游走算法在相邻切片上分割前列腺。我们传播该过程，直到所有切片都被分割。在32个三维经直肠超声图像的分割方法进行了测试。放射科医师的手动分割作为验证的金标准。实验结果表明，该方法获得了91.37± 0.05%的Dice相似系数。该分割方法可以应用于3D超声引导的前列腺活检和其他应用。
This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision tree as the classifier to distinguish the prostate from the non-prostate tissue because of its fast speed and superior performance, especially for a binary classification problem. Our segmentation algorithm is initialized with the user roughly marking the prostate and non-prostate points on the mid-gland slice which are fitted into an ellipse for obtaining more points. Based on these fitted seed points, we run the random walk algorithm to segment the prostate on the mid-gland slice. The segmented contour and the information from the decision tree classification are combined to determine the initial seed points for the other slices. The random walk algorithm is then used to segment the prostate on the adjacent slice. We propagate the process until all slices are segmented. The segmentation method was tested in 32 3D transrectal ultrasound images. Manual segmentation by a radiologist serves as the gold standard for the validation. The experimental results show that the proposed method achieved a Dice similarity coefficient of 91.37±0.05%. The segmentation method can be applied to 3D ultrasound-guided prostate biopsy and other applications.