3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts

Abstract

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.