3D microscopic-imaging has numerous fields of application in biology and medicine, e.g. to analyze model organisms like mouse [1], zebrafish [2] or fruit fly [3]. An important aim is to reconstruct 3D surfaces or volumes from a set of 2D images called stack. As the manual annotation is time-consuming, automated image processing is applied to identify and quantify specific structures known as segments. For data sets with homogeneous segments, high-contrast and clear edges, there are plenty of sophisticated methods and tools to automatically annotate and quantify these segments (e.g. [4], [5], [6], [7]).
However, if image quality is low or if connected structures change rapidly across the slices of a stack, an accurate automated segmentation is impossible. Although, there are interactive software packages for segmentation, either they require good image quality [7] or contain only a few automatic segmentation methods [8]. Thus, effort is required to either perform the segmentation manually or to correct inaccurate automatic segmentation results, which limits accurate segmentation possibilities of 3D image stacks in high-throughput.
Figure 1 shows an exemplary electron microscopy (EM) image of a neuromuscular junction in mouse. An automatic segmentation and forecast of the edges is impossible due to the variable contrast, filigree structures of interest and non-smooth edge transitions across the slices. However, a highly accurate segmentation is needed, to visualize and analyze the folded membrane in 3D and to derive new insights about the 3D structure and the signal transmission at the neuromuscular junction.
Figure 1 Electron microscopic image of the neuromuscular junction in mouse. Colored lines are results of a manual and a semi-automatic LiveWire segmentation. Regions below and above the segmentation lines are the pre- and postsynapse, respectively (adapted from [9]).
An idea to increase segmentation accuracy is to support automatic algorithms with ground truth given by experts. In [9], we presented such a workflow, introducing a semi-automatic method based on the LiveWire technique [10], [11] (Figure 1): The original grayscale image is filtered using an objectness filter [12] and a subsequent binarization optimizes the edges, such that a semi-automatic segmentation approach can be optimally supported. The user is asked to manually click a few points along the structures of interest in the grayscale image and the LiveWire algorithm automatically connects these points by searching for the shortest path between neighboring points in the filtered binary image.
In the present paper, we use the discussed semi-automatic segmentation and extend it to support and accelerate the 3D segmentation. We add a minimal amount of manual input, to finally extract high-quality 3D surfaces of structures of interest from large EM images. Semi-automatically annotated segments are used to forecast the segmentation to adjacent slices by automatically projecting a subset of the contour pixels to the next slice. Projected pixels are again automatically connected using the LiveWire approach. Wrong propagations can be corrected by using a higher number of click points or by manually moving erroneous segments to the correct positions. Corrected segmentations yield to further ground truth which can then be propagated to adjacent slices. There is no need for parameter modifications, which allows non-experts to operate the tool.
![Electron microscopic image of the neuromuscular junction in mouse. Colored lines are results of a manual and a semi-automatic LiveWire segmentation. Regions below and above the segmentation lines are the pre- and postsynapse, respectively (adapted from [9]).](/viewimg/j/cdbme.2016.2.issue-1/cdbme-2016-0097/cdbme-2016-0097.xml?img=graphic/j_cdbme-2016-0097_fig_001.jpg)
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