  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.
