Detecting various types of cells in and aroundthe tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

BACKGROUND Cannabinoid (CB) receptors are involved in the regulation of gastrointestinal (GI) motility under physiological and pathophysiological conditions. We aimed to characterize the possible influence of CB(1) and CB(2) receptors on motility impairment in a model of septic ileus. METHODS Lipopolysaccharide (LPS) injections were used to mimic pathophysiological features of septic ileus. Spontaneous jejunal myoelectrical activity was measured in rats in vivo, and upper GI transit was measured in vivo by gavaging of a charcoal marker into the stomach of mice, in absence or presence of LPS, and CB(1) and CB(2) receptor agonists and antagonists. Tumour necrosis factor (TNF)-alpha and interleukin (IL)-6 levels were measured using enzyme-linked immunosorbent assay. Histology was performed with haematoxylin-eosin staining. KEY RESULTS Lipopolysaccharide treatment significantly reduced amplitude and frequency of myoelectric spiking activity and GI transit in vivo in a dose-dependent manner. TNF-alpha and IL-6 were increased in LPS-treated animals and histology showed oedema and cell infiltration. Both, the CB(1) agonist HU210 and the CB(2) agonist JWH133 reduced myoelectrical activity whereas the CB(1) antagonist AM251 caused an increase of myoelectrical activity. Pretreatment with AM251 or AM630 prevented against LPS-induced reduction of myoelectrical activity, and also against the delay of GI transit during septic ileus in vivo. CONCLUSIONS & INFERENCES The LPS model of septic ileus impairs jejunal myoelectrical activity and delays GI transit in vivo. Antagonists at the CB(1) receptor or the CB(2) receptor prevent the delay of GI transit and thus may be powerful tools in the future treatment of septic ileus.

Background The stomata of plants mainly regulate gas exchange and water dispersion between the interior and external environments of plants and play a major role in the plants’ health. The existing methods of stomata segmentation and measurement are mostly for specialized plants. The purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants. The proposed method utilizes level set theory and image processing technology and can outperform the existing stomata segmentation and measurement methods based on threshold and skeleton in terms of its versatility. Results The single stomata images of different plants were the input of the method and a level set based on the Chan-Vese model was used for stomatal segmentation. This allowed the morphological features of the stomata to be measured. Contrary to existing methods, the proposed segmentation method does not need any prior information about the stomata and is independent of the plant types. The segmentation results of 692 living stomata of black poplars show that the average measurement accuracies of the major and minor axes, area, eccentricity and opening degree are 95.68%, 95.53%, 93.04%, 99.46% and 94.32%, respectively. A segmentation test on dayflower ( Commelina benghalensis ) stomata data available in the literature was completed. The results show that the proposed method can effectively segment the stomata images (181 stomata) of dayflowers using bright-field microscopy. The fitted slope of the manually and automatically measured aperture is 0.993, and the R 2 value is 0.9828, which slightly outperforms the segmentation results that are given in the literature. Conclusions The proposed automated segmentation and measurement method for living stomata is superior to the existing methods based on the threshold and skeletonization in terms of versatility. The method does not need any prior information about the stomata. It is an unconstrained segmentation method, which can accurately segment and measure the stomata for different types of plants (woody or herbs). The method can automatically discriminate whether the pore region is independent or not and perform pore region extraction. In addition, the segmentation accuracy of the method is positively correlated with the stomata’s opening degree.

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