Semi-Automatic And Automatic Ki-67 Index Examination In Whole Slide Images Of Meningiomas

Introduction/ Background Histological examination of tissue subjects by immunohistochemical staining is the basic method of recognizing types of cancer and it provides valuable indicators concerning choice of optimal therapy or defining the prognosis. One of a most important markers is the mitotic receptor Ki-67, among others, in meningiomas [1]. According to examination guidelines, ROI’s (Region of interest) whose fields correspond with the high positive receptors’ reaction should be selected. Aims The aim of this paper is a compare of Ki-67 index examination in meningioma specimens performed on the whole slide images(WSI) in two ways: with selection of hot-spot regions by the experts, and with automatic se- lection of hot-spots. Using both ways we have analyzed variability of results between two experts and between the experts and the automatic procedure, also in respect of Ki-67 level.   Methods The fifty cases of meningiomas were stained with the ready-to-use FLEX Ki-67 antigen (Dako, code IR626) in Dako Autostainer Link. Acquisition of WSIs was carried out by the 3DHistech Pannoramic 250 Flash II scanner under the 20x magnification of lens. The selection of hot-spots was done manually by two experts and automatically with the proposed method of automatic hot-spot detection. The suggested WSI processing scheme was based on the following steps: • defining the map of specimen using the thresholding procedure and morphological filtering, • eliminating the areas containing blood cells (hemorrhages) by the texture analysis (Unser features) and classification, • eliminating the specinem folds by the texture analysis (Unser and Local Binary Patterns) and classification, • selecting sequential fields of the hot-spots based  on cells segmentation and the punishment function to avoid excessive proximity, and it is the extention of idea presented in paper [2]. The final analysis of Ki-67 index was performed on the full resolution images with the same procedure of image analysis.   Results The results indicated that the mean difference between the Ki-67 index of Expert A and Expert B was -0.6065% (SD ±1.27%). Comparison between the results of Automatic system and Expert A gives mean difference 0.5207% (SD 1.18%) whereas in relation to the Exert B, it was -0.0858% (SD 1.21%). No significant skewness was observed in any of Bland-Altman plots.   The determination analysis gives R2 equals 0.947 (Expert A to Expert B), 0.947 (System to Expert A), and 0.944 (System to Expert B), all p<0.000001. The automatic procedure for the hot-spot detection in meningioma WSI gives the high concordance of results with the expert’s examinations. The differences between the automatic and both experts’ results are included in the range of variability of experts’ results. The presented results confirm that the proposed automatic procedure can be introduced to the multicenter verification process for practical applicability in histopathological diagnosis in the near future. This work has been supported by the National Centre for Research and Development (PBS2/ A9/21/2013 grant), Poland.

Prompt K_short production in pp collisions at sqrt(s)=0.9 TeV

The production of K_short mesons in pp collisions at a centre-of-mass energy of 0.9 TeV is studied with the LHCb detector at the Large Hadron Collider. The luminosity of the analysed sample is determined using a novel technique, involving measurements of the beam currents, sizes and positions, and is found to be 6.8 +/- 1.0 microbarn^-1. The differential prompt K_short production cross-section is measured as a function of the K_short transverse momentum and rapidity in the region 0 < pT < 1.6 GeV/c and 2.5 < y < 4.0. The data are found to be in reasonable agreement with previous measurements and generator expectations.Comment: 6+18 pages, 6 figures, updated author lis

Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model

The pathologists follow a systematic and partially manual process to obtain histological tissue sections from the biological tissue extracted from patients. This process is far from being perfect and can introduce some errors in the quality of the tissue sections (distortions, deformations, folds and tissue breaks). In this paper, we propose a deep learning (DL) method for the detection and segmentation of these damaged regions in whole slide images (WSIs). The proposed technique is based on convolutional neural networks (CNNs) and uses the U-net model to achieve the pixel-wise segmentation of these unwanted regions. The results obtained show that this technique yields satisfactory results and can be applied as a pre-processing step for automatic WSI analysis in order to prevent the use of the damaged areas in the evaluation processes

Measurement of sigma (pp -> bbX) at √s=7 TeV in the forward region

Decays of b hadrons into final states containing a D-0 meson and a muon are used to measure the bb; production cross-section in proton-proton collisions at a centre-of-mass energy of 7 TeV at the LHC. In the pseudorapidity interval 2 < eta < 6 and integrated over all transverse momenta we find that the average cross-section to produce b-flavoured or b-flavoured hadrons is (75.3 +/- 5.4 +/- 13.0) mu b

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies

In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3(+) cell density within scarred regions and higher CD3(+) cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies

Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning

Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation. Patients were stratified for IFTA development (&amp;lt;10% versus &amp;gt;= 10%) from 6 weeks to 6 months post-transplantation, based on histopathological assessment by three kidney pathologists. One mTSA panel was developed for visualization of capillaries, T- and B-lymphocytes and macrophages and a second mTSA panel for T-helper cell and macrophage subsets. The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI). We used an existing CNN for the detection of lymphocytes with cytoplasmatic staining patterns in immunohistochemistry and developed two new CNNs for the detection of macrophages and nuclear-stained lymphocytes. F1-scores were 0.77 (nuclear-stained lymphocytes), 0.81 (cytoplasmatic-stained lymphocytes), and 0.82 (macrophages) on a test set of artificial brightfield WSI. The CNNs were used to detect inflammatory cells, after which we assessed the peritubular capillary extent, cell density, cell ratios, and cell distance in the two patient groups. In this cohort, distance of macrophages to other immune cells and peritubular capillary extent did not vary significantly at 6 weeks post-transplantation between patient groups. CD163(+) cell density was higher in patients with &amp;gt;= 10% IFTA development 6 months post-transplantation (p &amp;lt; 0.05). CD3(+)CD8(-)/CD3(+)CD8(+) ratios were higher in patients with &amp;lt;10% IFTA development (p &amp;lt; 0.05). We observed a high correlation between CD163(+) and CD4(+)GATA3(+) cell density (R = 0.74, p &amp;lt; 0.001). Our study demonstrates that CNNs can be used to leverage reliable, quantitative results from mTSA-stained, multi spectrally imaged slides of kidney transplant biopsies. This study describes a methodology to assess the microenvironment in sparse tissue samples. Deep learning, multiplex immunohistochemistry, and mathematical image processing techniques were incorporated to quantify lymphocytes, macrophages, and capillaries in kidney transplant biopsies of delayed graft function patients. The quantitative results were used to assess correlations with development of interstitial fibrosis and tubular atrophy.Funding Agencies|ERACoSysMed initiative (project SysMIFTA) as part of the European Unions Horizon 2020 Framework Programme by ZonMw [9003035004]; German Ministry of Research and Education (BMBF)Federal Ministry of Education &amp; Research (BMBF) [FKZ031L-0085A, FKZ01ZX1710A, FKZ01ZX1608A]; Dutch Kidney Foundation (project DEEPGRAFT) [17OKG23]</p

Species diversification – which species should we use?

Large detector systems for particle and astroparticle physics; Particle tracking detectors; Gaseous detectors; Calorimeters; Cherenkov detectors; Particle identification methods; Photon detectors for UV. visible and IR photons; Detector alignment and calibration methods; Detector cooling and thermo-stabilization; Detector design and construction technologies and materials. The LHCb experiment is dedicated to precision measurements of CP violation and rare decays of B hadrons at the Large Hadron Collider (LHC) at CERN (Geneva). The initial configuration and expected performance of the detector and associated systems. as established by test beam measurements and simulation studies. is described. © 2008 IOP Publishing Ltd and SISSA