Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the centers of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and potentially lead to a better understanding of cancer.

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

In this paper, we present a stochastic model for glandular structures in histology images of tissue slides stained with Hematoxylin and Eosin, choosing colon tissue as an example. The proposed Random Polygons Model (RPM) treats each glandular structure in an image as a polygon made of a random number of vertices, where the vertices represent approximate locations of epithelial nuclei. We formulate the RPM as a Bayesian inference problem by defining a prior for spatial connectivity and arrangement of neighboring epithelial nuclei and a likelihood for the presence of a glandular structure. The inference is made via a Reversible-Jump Markov chain Monte Carlo simulation. To the best of our knowledge, all existing published algorithms for gland segmentation are designed to mainly work on healthy samples, adenomas, and low grade adenocarcinomas. One of them has been demonstrated to work on intermediate grade adenocarcinomas at its best. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular structures in histology images of normal human colon tissues as well as benign and cancerous tissues, excluding undifferentiated carcinomas.

Aims: Digital pathology (DP) offers advantages over glass slide microscopy (GS), but data demonstrating a statistically valid equivalent (i.e. non‐inferior) performance of DP against GS are required to permit its use in diagnosis. The aim of this study is to provide evidence of non‐inferiority.Methods and results: Seventeen pathologists re‐reported 3017 cases by DP. Of these, 1009 were re‐reported by the same pathologist, and 2008 by a different pathologist. Re‐examination of 10 138 scanned slides (2.22 terabytes) produced 72 variances between GS and DP reports, including 21 clinically significant variances. Ground truth lay with GS in 12 cases and with DP in nine cases. These results are within the 95% confidence interval for existing intraobserver and interobserver variability, proving that DP is non‐inferior to GS. In three cases, the digital platform was deemed to be responsible for the variance, including a gastric biopsy, where Helicobacter pylori only became visible on slides scanned at the ×60 setting, and a bronchial biopsy and penile biopsy, where dysplasia was reported on DP but was not present on GS.Conclusions: This is one of the largest studies proving that DP is equivalent to GS for the diagnosis of histopathology specimens. Error rates are similar in both platforms, although some problems e.g. detection of bacteria, are predictable.

Context: α-Klotho has emerged as a powerful regulator of the aging process. To date, the expression profile of α-Klotho in human tissues is unknown, and its existence in some human tissue types is subject to much controversy. Objective: This is the first study to characterize systemwide tissue expression of transmembrane α-Klotho in humans. We have employed next-generation targeted proteomic analysis using parallel reaction monitoring in parallel with conventional antibody-based methods to determine the expression and spatial distribution of human α-Klotho expression in health. Results: The distribution of α-Klotho in human tissues from various organ systems, including arterial, epithelial, endocrine, reproductive, and neuronal tissues, was first identified by immunohistochemistry. Kidney tissues showed strong α-Klotho expression, whereas liver did not reveal a detectable signal. These results were next confirmed by Western blotting of both whole tissues and primary cells. To validate our antibody-based results, α-Klotho-expressing tissues were subjected to parallel reaction monitoring mass spectrometry (data deposited at ProteomeXchange, PXD002775) identifying peptides specific for the full-length, transmembrane α-Klotho isoform. Conclusions: The data presented confirm α-Klotho expression in the kidney tubule and in the artery and provide evidence of α-Klotho expression across organ systems and cell types that has not previously been described in humans.

Context: The adipokine leptin has critical importance in central appetite regulation. In contrast to some suggestion of adiponectin influencing energy homeostasis in rodents, there is no evidence for adiponectin or resistin entering the human blood-brain barrier. Objective: The objective was to establish the presence of adiponectin or resistin in human cerebrospinal fluid (CSF) and to compare their distribution with leptin. Furthermore, we wished to examine the expression of the adiponectin receptors 1 and 2 (AdipR1, AdipR2) in the human hypothalamus. Methods: For this purpose, serum and CSF samples were collected from 20 men and 19 women matched for age [men, 69.8 ± 8.6 yr (mean ± sd); women, 69.4 ± 4.3 yr] and BMI (men, 29.4 ± 3.4 kg/m²; women, 27.3 ± 4.8 kg/m²) undergoing elective surgery under spinal anesthesia. Results: Adiponectin was identified in CSF with levels 1000-fold less than serum, whereas resistin and leptin levels were 100-fold less. Unlike their serum levels, adiponectin CSF levels showed no gender difference or correlation with insulin resistance, which is similar to resistin CSF levels. The adiponectin and leptin CSF/serum ratios in our study exhibit the same pattern of gender-specific BMI association with inverse correlation in women (r = −0.61; P = 0.02) and no correlation in men (r = 0.026; P = not significant). Furthermore, immunostaining established AdipR1 and -2 in the hypothalamus and increased AdipR2 expression in the paraventricular nucleus, which is involved in energy regulation. Conclusion: In summary, our findings show both the presence of adiponectin and resistin in human CSF, with no effect of insulin resistance on CSF levels. The CSF entry of adiponectin and leptin in women appears to be impaired in obesity.