text: Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems. In this work, the focus is on the segmentation of the glomeruli constituting a highly relevant structure in renal histopathology, which has not been investigated before in combination with CNNs. We propose two different CNN cascades for segmentation applications with sparse objects. These approaches are applied to the problem of glomerulus segmentation and compared with conventional fully-convolutional networks. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained. Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to recent approaches. In conclusion, we can state that especially one of the proposed cascade networks proved to be a highly powerful tool for segmenting the renal glomeruli providing best segmentation accuracies and also keeping the computing time at a low level.
  @cite_12: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining.
summary: Few approaches were proposed to perform segmentation of the glomeruli. @cite_12 introduced a separate detection and segmentation stage, both based on training a supervised classification model, specifically a linear support vector machine. For detection and segmentation different versions of histogram of oriented gradient descriptors were applied. Detection was performed by means of the sliding window approach. For segmentation, a polygon-fitting technique was utilized to determine the precise outline of the objects by adding a circularity constraint. For learning to distinguish between border region and non-border regions (and not between inside and outside of a glomerulus), this method requires highly precise annotations for training. This method was developed for a specific staining (desmin) which highlights cells in glomeruli.