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_13: In this paper we compare different approaches to combine color and statistical texture descriptors. Previous studies on this topic were conducted on natural images only. We focus on the particular case of histological datasets where color plays an important role due to the staining process of the biological samples. We also introduce two new variants of the well-known Local Binary Patterns (LBP) operator. We test these approaches on three diversified histological datasets. We show that combining color and texture features extracted separately is preferable on datasets having a large variability in the staining, while simultaneous extraction of color and texture information is recommended only for more standardized stainings.
  @cite_5: Contextual information plays an important role in solving vision problems such as image segmentation. However, extracting contextual information and using it in an effective way remains a difficult problem. To address this challenge, we propose a multi-resolution contextual framework, called cascaded hierarchical model (CHM), which learns contextual information in a hierarchical framework for image segmentation. At each level of the hierarchy, a classifier is trained based on down sampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. We repeat this procedure by cascading the hierarchical framework to improve the segmentation accuracy. Multiple classifiers are learned in the CHM, therefore, a fast and accurate classifier is required to make the training tractable. The classifier also needs to be robust against over fitting due to the large number of parameters learned during training. We introduce a novel classification scheme, called logistic disjunctive normal networks (LDNN), which consists of one adaptive layer of feature detectors implemented by logistic sigmoid functions followed by two fixed layers of logical units that compute conjunctions and disjunctions, respectively. We demonstrate that LDNN outperforms state-of-the-art classifiers and can be used in the CHM to improve object segmentation performance.
  @cite_10: The advancing pervasion of digital pathology in research and clinical practice results in a strong need for image analysis techniques in the field of histopathology. Due to diverse reasons, histopathological imaging generally exhibits a high degree of variability. As automated segmentation approaches are known to be vulnerable, especially to unseen variability, we investigate several stain normalization methods to compensate for variations between different whole slide images. In a large experimental study, we investigate all combinations of five image normalization (not only stain normalization) methods as well as five image representations with respect to the classification performance in two application scenarios in kidney histopathology. Finally, we also pose the question, if color normalization is sufficient to compensate for the changed properties between whole slide images in an application scenario with few training data.
  @cite_4: A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for observing the evolution of 3 forms of idiopathic nephrotic syndrome, including developing predictors for progression of kidney disease. The knowledge of future kidney function is desirable as it can identify high-risk patients and influence treatment decisions, reducing the likelihood of irreversible kidney decline.
summary: @cite_5 applied a CNN for distinguishing between glomerulus and non-glomerulus texture based on previously extracted patches (similar to @cite_10 @cite_13 ). Although this application is distinct from a segmentation, it provides evidence for the principal effectiveness of CNNs for distinguishing between glomerulus and non-glomerulus tissue. Neural networks were also applied by @cite_4 with focus on a regression on WSI level. Specifically, the authors focussed on directly predicting the kidney function based on biopsies from patients suffering from chronic kidney disease without previously segmenting the glomeruli.