Segmentation of vessel structures in serial whole slide sections using region-based context features.

We present a method for the automatic segmentation of vascular structures in stacks of serial sections. It was initially motivated within the Virtual Liver Network research project that aims at creating a multi-scale virtual model of the liver. For this the vascular systems of several murine livers under different conditions need to be analyzed. To get highly detailed datasets, stacks of serial sections of the whole organs are prepared. Due to the huge amount of image data an automatic approach for segmenting the vessels is required. After registering the slides with an established method we use a set of Random Forest classifiers to distinguish vessels from tissue. Instead of a pixel-wise approach we perform the classification on small regions. This allows us to use more meaningful features. Besides basic intensity and texture features we introduce the concept of context features, which allow the classifiers to also consider the neighborhood of a region. Classification is performed in two stages. In the second stage the previous classification result of a region and its neighbors is used to refine the decision for a particular region. The context features and two stage classification process make our method very successful. It can handle different stainings and also detect vessels in which residue like blood cells remained. The specificity reaches 95%-99% for pure tissue, depending on staining and zoom level. Only in the direct vicinity of vessels the specificity declines to 88%-96%. The sensitivity rates reach between 89% and 98%.