Cell-graph mining for breast tissue modeling and classification

We consider the problem of automated cancer diagnosis in the context of breast tissues. We present graph theoretical techniques that identify and compute quantitative metrics for tissue characterization and classification. We segment digital images of histopatological tissue samples using k-means algorithm. For each segmented image we generate different cell-graphs using positional coordinates of cells and surrounding matrix components. These cell-graphs have 500-2000 cells(nodes) with 1000-10000 links depending on the tissue and the type of cell-graph being used. We calculate a set of global metrics from cell-graphs and use them as the feature set for learning. We compare our technique, hierarchical cell graphs, with other techniques based on intensity values of images, Delaunay triangulation of the cells, the previous technique we proposed for brain tissue images and with the hybrid approach that we introduce in this paper. Among the compared techniques, hierarchical-graph approach gives 81.8% accuracy whereas we obtain 61.0%, 54.1% and 75.9% accuracy with intensity-based features, Delaunay triangulation and our previous technique, respectively. © 2007 IEEE

A combined anatomical variation of inferior epigastric vessels

During the routine anatomical dissection of a male cadaver, a variation was observed both in the inferior epigastric artery (IEA) and inferior epiastric vein (IEV). Although the origin of the IEA from the right femoral artery (FA) is common variation in this case, the right IEA originated from the RFA, 13 mm inferior to inguinal ligament. The artery didn’t course anterior to the femoral vein (FV) as described in the variations of this vessel; instead, coursed on the lateral side of the variant IEV. Additionally, in this cadaver, the single right IEV drained to RFV 8 mm inferior to inguinal ligament. Both the variant artery and vein passed posterior to spermatic cord and their course in the rectus sheath were normal in every aspect. Due to its clinical importance, this combined anatomical variation must be remembered by the surgeons

Association between frontal sinus development and persistent metopic suture

Background: Frontal sinuses are 2 irregular cavities, placed between 2 lamina of frontal bone. Expansion continues during childhood and reaches full size after puberty. Persistent metopic suture is one of the factors that are related to abnormal frontal sinus development. In this study, we want to discuss about the coexistence of persistent metopic suture and abnormal frontal sinus development using radiological techniques.Materials and methods: In this retrospectively planned study, images of 631 patients were examined, 217 (34.4%) of them were men and 414 (65.6%) of them were women. Brain computed tomography and magnetic resonance images were retrieved from the electronic archive for analysis.Results: In this study, frontal sinus development is categorised as right side atrophy, left side atrophy, bilateral atrophy and bilaterally developed sinuses. The presence of metopic suture was accepted as persistent metopic suture. Frontal sinus atrophy was found in 22.7% and persistent metopic sutures were found in 9.7% of overall.Conclusions: In this study, no significant results were detected that were relatedto the frontal sinus agenesis or dismorphism associated with persistent metopicsuture. We conclude that, although publications propounding metopism thatleads to abnormal frontal sinus development are present in the literature, noreasonable explanation has been mentioned in these articles; and we believe thatthese findings are all incidental.

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

Coupled Analysis of In Vitro and Histology Tissue Samples to Quantify Structure-Function Relationship

The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. However, at the tissue level of biological complexity we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological tissues at the cellular and subcellular level, we cannot assemble it in a way that explains the correspondingly complex biological functions these structures perform. To help close this information gap we define here several quantitative temperospatial features that link tissue structure to its corresponding biological function. Both histological images of human tissue samples and fluorescence images of three-dimensional cultures of human cells are used to compare the accuracy of in vitro culture models with their corresponding human tissues. To the best of our knowledge, there is no prior work on a quantitative comparison of histology and in vitro samples. Features are calculated from graph theoretical representations of tissue structures and the data are analyzed in the form of matrices and higher-order tensors using matrix and tensor factorization methods, with a goal of differentiating between cancerous and healthy states of brain, breast, and bone tissues. We also show that our techniques can differentiate between the structural organization of native tissues and their corresponding in vitro engineered cell culture models

Quantification of Spatial Parameters in 3D Cellular Constructs Using Graph Theory

Multispectral three-dimensional (3D) imaging provides spatial information for biological structures that cannot be measured by traditional methods. This work presents a method of tracking 3D biological structures to quantify changes over time using graph theory. Cell-graphs were generated based on the pairwise distances, in 3D-Euclidean space, between nuclei during collagen I gel compaction. From these graphs quantitative features are extracted that measure both the global topography and the frequently occurring local structures of the “tissue constructs.” The feature trends can be controlled by manipulating compaction through cell density and are significant when compared to random graphs. This work presents a novel methodology to track a simple 3D biological event and quantitatively analyze the underlying structural change. Further application of this method will allow for the study of complex biological problems that require the quantification of temporal-spatial information in 3D and establish a new paradigm in understanding structure-function relationships

Reclaiming the political : emancipation and critique in security studies

The critical security studies literature has been marked by a shared commitment towards the politicization of security – that is, the analysis of its assumptions, implications and the practices through which it is (re)produced. In recent years, however, politicization has been accompanied by a tendency to conceive security as connected with a logic of exclusion, totalization and even violence. This has resulted in an imbalanced politicization that weakens critique. Seeking to tackle this situation, the present article engages with contributions that have advanced emancipatory versions of security. Starting with, but going beyond, the so-called Aberystwyth School of security studies, the argument reconsiders the meaning of security as emancipation by making the case for a systematic engagement with the notions of reality and power. This revised version of security as emancipation strengthens critique by addressing political dimensions that have been underplayed in the critical security literature

A Tale of Two Shares: Why Two-Share Threshold Implementation Seems Worthwhile-and Why it is Not

In this work, we explore the possibilities for practical Threshold Implementation (TI) with only two shares in order for a smaller design that needs less randomness but is still first-order leakage resistant. We present the first two-share Threshold Implementations of two lightweight block ciphers---Simon and Present. The implementation results show that two-share TI gains in compactness while loses in throughput compared with three-share schemes. Moreover, the leakage analyses show that two-share TI retains perfect first-order resistance but is shadowed by a strong second-order leakage, making it less worthwhile

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