Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues

Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states

<p>Abstract</p> <p>Background</p> <p>Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.</p> <p>Methods</p> <p>In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the <it>nodes </it>and the approximate adjacency of cells are represented with <it>edges</it>. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins.</p> <p>Results</p> <p>We built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies.</p> <p>Conclusions</p> <p>Together, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.</p

Cell-Graph Mining for Breast Tissue Modelling and Classification

Motivation: The most reliable way in the current practice of medicine to diagnose cancer is the pathological examination of a biopsy which has a certain level of subjectivity. To reduce this subjectivity and have a mathematical model for diagnosing cancer tissues we consider the problem of automated cancer diagnosis in the context of breast cancer tissues. Summary: This work presents a graph theoretical technique that identifies and computes quantitative metrics for tissue characterization and classification. We segmented the digital images of histopatological tissue samples having 10 × 14 magnification and 960 × 960 pixels. Then for each image we generated cell-graphs using positional coordinates of cells and surrounding matrix components. These cellgraphs have 500-2000 cells(nodes) with 1000-10000 links depending on tissue and the type of the cell-graph being used. We’ve calculated a set of global metrics from cell-graphs and used them as the feature set for learning. Results: We compared our technique with other learning techniques based on intensity values of images, voronoi diagrams of the cells, and the previous technique we proposed for brain tissue images. Among the compared techniques our approach gave %79.1 accuracy whereas we obtained learning ratios of %49.2, %54.1 and %75.9 with intensity based features, voronoi diagrams and our previous technique, respectively

The Association of Normal Tension Glaucoma with Buerger'S Disease: A Case Report

Background: To report a case of a 48-year-old man with Buerger's disease who presented with bilateral normal-tension glaucoma (NTG). Case presentation: A 48-year-old man who had been diagnosed with Buerger's disease 12 years ago, and received bilateral below-the-knee amputations for ischemic ulcers of the lower limbs, presented at our clinic due to a sudden loss of visual acuity in the left eye. A fundus exam revealed a cup-to-disc ratio of 0.5 for the right eye and 0.8 for the left eye, arteriolar constriction in both eyes, retinal edema in the inferopapillary area, and splinter hemorrhages and soft exudate in the left eye. We diagnosed the patient as having acute nasal branch retinal artery occlusion in the left eye and bilateral NTG, as a result of the ophthalmologic examination and the other findings. Conclusion: Although the pathomechanism of NTG is still unknown, previous studies have suggested that patients with NTG show a higher prevalence of vasospastic disorders. We present the second report of NTG associated with Buerger's disease to be described in the literature.WoSScopu