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首页> 外文会议>Computational Intelligence for Multimedia Signal and Vision Processing, 2009. CIMSVP '09 >3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts
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3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts

机译：通过前向和后向图割提取3D体积的EM数据堆栈中密集排列的单元格

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3D reconstruction on dense nanoscale medical images is a very challenging research topic. The challenge comes from the fact that boundaries of objects on such images are not always very clear due to imperfect staining. This makes the segmentation of dense nanoscale medical images very difficult and thus increases the difficulty in 3D reconstruction. In this paper, we proposed a method based on watershed and an interactive segmentation technique, graph cuts, to extract 3D volumes from dense nanoscale medical images. In our method, images are first segmented by a marker-controlled watershed algorithm. Markers for watershed segmentation algorithm are seed points generated by using distance transform, followed by a new grouping method that clusters seed points that are too close. Regions obtained by watershed transform segmentation algorithms are considered as nodes in a graph. Edges are to connect between the nodes in adjacent image slices. The weight on each edge is defined based on the overlapped area between nodes. User-selected nodes (regions) in an initial image slice serve as hard constraints in the minimization process. A globally optimal 3D volume is obtained by minimizing MAP-MRF energy function via graph cuts. In our application, in order to obtain a complete 3D volume structures including branching, the final 3D volume is the union of two 3D volumes obtained by performing the minimization of MAP-MRF energy function using graph cuts forwards and backwards through the image stack. Experiments are conducted both on synthetic data and on nanoscale image sequences from the serial block face scanning electron microscope (SBF-SEM). The results show that our method can successfully extract 3D volumes.
机译：在密集的纳米级医学图像上进行3D重建是一个非常具有挑战性的研究主题。挑战来自以下事实：由于不完美的染色，此类图像上的对象边界并不总是很清晰。这使得密集的纳米级医学图像的分割非常困难，从而增加了3D重建的难度。在本文中，我们提出了一种基于分水岭和交互式分割技术（图割）的方法，用于从密集的纳米级医学图像中提取3D体积。在我们的方法中，首先通过标记控制的分水岭算法对图像进行分割。分水岭分割算法的标记是通过使用距离变换生成的种子点，然后是一种新的分组方法，该方法将过于靠近的种子点聚类。通过分水岭变换分割算法获得的区域被视为图中的节点。边缘将连接相邻图像切片中的节点之间。基于节点之间的重叠区域定义每个边缘上的权重。初始图像切片中用户选择的节点（区域）在最小化过程中充当硬约束。通过图形切割使MAP-MRF能量函数最小化，可以获得全局最佳3D体积。在我们的应用程序中，为了获得包括分支在内的完整3D体积结构，最终3D体积是两个3D体积的并集，这两个3D体积是通过使用向前和向后穿过图像堆栈的图形切割执行MAP-MRF能量函数的最小化而获得的。实验是对合成数据和来自串行块面扫描电子显微镜（SBF-SEM）的纳米级图像序列进行的。结果表明，我们的方法可以成功提取3D体积。

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