PCA Based Bayesian Approach for Automatic Multiple Sclerosis Lesion Detection

The classical Bayes rule plays very important role in the field of lesion identification. However, the Bayesian approach is very difficult in high dimensional spaces for lesion detection. An alternative approach is Principle Component Analysis (PCA) for automatic multiple sclerosis lesion detection problems in high dimensional spaces. In this study, PCA based Bayesian approach is explained for automatic multiple sclerosis lesion detection using Markov Random Fields (MRF)and Singular Value Decomposition (SVD). It is shown that PCA approach provides better understanding of data. Although Bayesian approach gives effective results, itis not easy to use in high dimensional spaces. Therefore, PCA based Bayesian detection will give much more accurate results for automatic multiple sclerosis (MS)lesion detection

Expectation Maximization And Gaussian Model Based Segmentation on Histology Slides

The importance of the Expectation Maximization (EM) algorithm isincreasing day by day in order to solve Maximum A Posteriori (MAP) estimation problems and Gaussian Mixture Models (GMMs), which are parametric probability density functions, have become more popular in computerized applications due tothe EM algorithm. This article explains an automatic GMM based image segmentation method for histology cell images. For this purpose, the GMM parameters, which are recomputed iteratively starting with initial values, arecalculated by using the EM algorithm which classifies each pixel into the class withthe largest probability distribution using maximum likelihood. The accuracy of this segmentation algorithm depends on how much close the probabilistic model to the gray level distributions of the input images

A level set method with Sobolev Gradient and Haralick Edge Detection

Variational level set methods, which have been proposed with various energy functionals, mostly use the ordinary L type gradient in gradient descent algorithm to minimize the energy functional. The gradient flow is influenced by both the energy to be minimized and the norms, which are induced from inner products, used to measure the cost of perturbation of the curve. However, there are many undesired properties related to the gradient flows due to the 2 L type inner products. For example, there is not any regularity term in the definition of this inner product that causes non-smooth flows and inaccurate results. Therefore, in this work, Sobolev gradient has been used that is more efficient than the 2 L type gradient for image segmentation and has powerful properties such as regular gradient flows, independency to parameterization of curves, less sensitive to local features and noise in the image and also faster convergence rate than the standard gradient. In addition, Haralick edge detector has been used instead of the edge indicator function in this study. Because, the traditional edge indicator function, which is the absolute of the gradient of the convolved image with the aussian function, is sensitive to noise in level set methods. Experimental results on real images , which are abdominal magnetic resonance images, have been obtained for spleen and kidney segmentation. Quantitative analyses have been performed by using different measurements to evaluate the performance of the proposed approach, which can ignore topological noises and detect boundaries successfully

Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma

<div><p>Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies. As a paradigm, we utilized second resections from patients diagnosed either with reactive brain changes (pseudoprogression) and recurrent glioblastoma (true progression). Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI-PTBP1 positive cells. PTBP1-positive nuclei, and the mean intensity value of PTBP1 signal in cells. Traditional pathological interpretation was unable to distinguish between groups due to unacceptably high discordance rates amongst expert neuropathologists. Our data demonstrated that recurrent glioblastoma showed more DAPI-PTBP1 positive cells and a higher mean intensity value of PTBP1 signal compared to resections from second surgeries that showed only reactive gliosis. Our work demonstrates the potential of utilizing automated image analysis to overcome the challenges of implementing fluorescent microscopy in diagnostic neuropathology.</p></div

PTBP1 antibody validation.

<p>(A) siRNA knock-down approach workflow for PTBP1 knockdown. (B) Western blotting of total cell lysates of the siRNA knockdown cells using anti-PTBP1 antibody. (C) Western blotting of total cell lysates from the siRNA knockdown cells using GAPDH as a loading control. (D) Densitometric analysis of western blotting data pooled from three independent experiments. (E) Immunofluorescent analysis of PTBP1 in scrambled siRNA treated (E1-E3) and anti-PTBP1 siRNA treated (F1-F3). (G) Work flow for cell plug formation to test PTBP1 in glioma. (H) Immunofluorescent stain of PTBP1 in glioma cell line. Cell lines used in B-D were LN229, and cells used for E-H were U251. Mean intensity gray values for PTBP1 knockdown cells were 40.4% decreased relative to scrambled transfected cells (mean PTBP1 density normalized to dapi density for scrambled treated = 0.71 (n = 75 cells), mean PTBP1 density normalized to dapi density for PTBP1 siRNA knockdown = 0.43 (n = 71 cells), p = 2.7 X 10⁻¹² by two-tailed homoscedastic T-test.</p

Cohen kappa coefficients for the concordance of the evaluation of pathological images.

<p>Cohen kappa coefficients for the concordance of the evaluation of pathological images.</p

PTBP1 expression in embryonic mouse brain.

<p>Mouse embryos were harvested from timed pregnant dames and immersion fixed in 4% PFA at gestational age E14. The ventricular lining is characterized as the zone of the neural stem cell niche that undergoes cell division. In these mitotic cells, PTBP1 localized to the cytoplasm and not the condensed chromatin in the neuroepithelium lining the lateral ventricles (A) and the cerebral aqueduct anlage of the midbrain (C). In the forebrain, PTBP expression was low-to-absent in neurons (B). Color codes are paced at the bottom left of each panel.</p

Objective image analysis demonstrates differences between groups.

<p>Objective image analysis metrics for DAPI_PTBP1 images, PTBP1-positive nuclei, and PTBP1 intensity are plotted in A-C, respectively. The median is demonstrated as the black line within the boxplot, the length of the boxplot represents the interquartile range, and the boxplot whiskers are 1.5 times the interquartile range. All datapoints outside the whiskers are considered outliers. Group to group comparisons for each analysis are performed by ANOVA and shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170991#pone.0170991.t002" target="_blank">Table 2</a>.</p

PTBP1 expression in postnatal mouse brain.

<p>Mice were perfused with 4% PFA and brains harvested for neuroanatomical analyses at postnatal day 7 (P7). Distinct anatomical regions in forebrain are denoted in boxes on the left. Hippocampal astrocytes (A-B), cerebral cortical astrocytes in layers I and II (E-F), and GFAP-positive neural progenitors in the ventricular lining (G-H) all showed strong PTBP1 expression throughout the nucleoplasm and in the perinucleolar compartment. PTBP1 expression was recued in neurons, with occasional NeuN-positive cortical pyramidal neurons showing weak PTBP1 expression in the perinucleolar compartment (C-D).</p

Diagnostic challenges at glioblastoma recurrence.

<p>A recurrent frontal lobe lesion highly suspicious for GB (A) is resected (B) and shows significant necrosis (C) with foci of cytologically atypical astrocytes (D). Standard immunohistochemical evaluation of FFPE tissue is shown in E-H. Molecular marker and interpretation are shown in the bottom left of each panel. Interpretation: Abundant necrosis, low ki67 labelling in the background of marked reactive gliosis and inflammatory cells suggested no significant disease progression in the tissue despite the concerning radiographic appearance. Methodology: Note, “standard IHC” refers to DAB 2nd antibody reaction precipitate followed by hematoxylin counterstain as shown in E-H.</p