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_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.
@cite_13 @cite_5 @cite_10 @cite_4
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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. 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. Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.