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_16: Systemic Lupus Erythematosus involvement of the kidneys is associated with high morbidity and mortality. Inter-observer variability can also lead to an incorrect assessment of the disease. Computer aided image analysis can potentially be used to advance the understanding, diagnosis and treatment of Lupus. Our research focuses on the image analysis of kidney biopsies for quantifying tissue characteristics relevant to Lupus. In this paper we describe an algorithm for identifying glomeruli in H&E stained mouse renal biopsies. The proposed approach uses color segmentation to identify all objects in the image that potentially correspond to the Bowman's space around the glomeruli. This is followed by a perceptual grouping procedure to group objects based on their size, location and orientation.
summary: To circumvent the need for large annotated training data, @cite_16 proposed an unsupervised segmentation algorithm. The authors aim at segmenting the Bowman's capsule surrounding the glomerulus by applying a color-based segmentation approach. The segmentation method consists of k-means clustering after thresholding including morphological operations. The inherent problem here is, that the Bowman's capsule is not always visible, depending on the staining protocol as well as the cutting plane (Fig. , ). Therefore, this method is not capable of generating a reliable precise segmentation of all objects.