Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams
Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams
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DOI：
10.1016/j.compmedimag.2019.101642
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发表时间：
2019-10-01
影响因子：
5.7
通讯作者：
Mori, Kensaku
中科院分区：
文献类型：
--
作者：
Wang, Chenglong;Roth, Holger R.;Mori, Kensaku
本文提出了一种利用Voronoi图精确估计肾血管优势区域的新方法。为了提供计算机辅助诊断的术前模拟肾部分切除术，我们必须获得信息的肾动脉和肾血管优势区域。我们提出了一种全自动分割方法，结合神经网络和基于张量的图切割方法，精确地提取肾脏和肾动脉。首先，我们使用卷积神经网络定位肾脏区域，并使用基于张量的图切割方法提取微小的肾动脉。然后，我们生成一个Voronoi图估计肾血管的优势区域的基础上分割的肾脏和肾动脉。8倍交叉验证的27例肾脏分割准确率达到95%的Dice评分。8例肾动脉分割准确率达到中心线重叠率80%。每个分区区域对应于肾血管优势区域。最终的优势区域估计精度达到了80%的骰子系数。临床应用表明，我们提出的估计方法在真实的临床手术环境中的潜力。进一步利用大规模数据库进行验证是我们未来的工作。(C)2019爱思唯尔有限公司版权所有。
This paper presents a new approach for precisely estimating the renal vascular dominant region using a Voronoi diagram. To provide computer-assisted diagnostics for the pre-surgical simulation of partial nephrectomy surgery, we must obtain information on the renal arteries and the renal vascular dominant regions. We propose a fully automatic segmentation method that combines a neural network and tensor-based graph-cut methods to precisely extract the kidney and renal arteries. First, we use a convolutional neural network to localize the kidney regions and extract tiny renal arteries with a tensor-based graph-cut method. Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries. The accuracy of kidney segmentation in 27 cases with 8-fold cross validation reached a Dice score of 95%. The accuracy of renal artery segmentation in 8 cases obtained a centerline overlap ratio of 80%. Each partition region corresponds to a renal vascular dominant region. The final dominant-region estimation accuracy achieved a Dice coefficient of 80%. A clinical application showed the potential of our proposed estimation approach in a real clinical surgical environment. Further validation using large-scale database is our future work. (C) 2019 Elsevier Ltd. All rights reserved.