Statistical shape modeling of multi-organ anatomies with shared boundaries

Introduction: Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some quantitative representation (such as correspondence points or landmarks) which can be used to study the covariance patterns of the shapes and answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within the shared boundaries of the heart can indicate potential pathologic changes such as right ventricular overload. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces.Methods: This work focuses on particle-based shape modeling (PSM), a state-of-art SSM approach for building shape models by optimizing the position of correspondence particles. The proposed PSM strategy for handling shared boundaries entails (a) detecting and extracting the shared boundary surface and contour (outline of the surface mesh/isoline) of the meshes of the two organs, (b) followed by a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population.Results: We demonstrate the shared boundary pipeline using a toy dataset of parameterized shapes and a clinical dataset of the biventricular heart models. The shared boundary model for the cardiac biventricular data achieves consistent parameterization of the shared surface (interventricular septum) and identifies the curvature of the interventricular septum as pathological shape differences

Comparison of Semantic Similarity Determination using Machine Learning

Evaluating the semantic similarity of two terms is a task central to automated understanding of natural languages. The challenge of “semantic similarity” lies in determining if two chunks of text have very similar meanings or totally different meanings. The amount of research on semantic similarity has increased greatly in the past 5 years, partially driven by the annual SemEval competitions. In this work, to compute the similarity between terms we consider the WordSim and SimLex data set, compare the results obtained between Neural network, Support Vector Machine and Linear Regression machine learning techniques and evaluate the results obtained against the M&C data set. For the data set considered, the Neural Network Model gave the best results, the Linear Regression method fared better than the Support Vector Machine with Regression

A CROSS-SECTIONAL STUDY ON ADHERENCE TO LIFESTYLE MODIFICATION AMONG KNOWN HYPERTENSIVE PATIENTS IN UDUPI DISTRICT

Objectives: Hypertension is the more prevalent non-communicable disease and is the major risk factors for the cardiovascular disease. Lifestyle modification plays a very important role in controlling or management of the hypertension. Hence, aim of this study is to assess the adherence and non-adherence to lifestyle modification among known hypertensive patients those who are visiting secondary care hospitals in Udupi District. Methods: This was a multi-centric hospital based cross-sectional study conducted at three secondary care hospitals. Samples were collected through convenient sampling. Standardized questionnaire used to collect data. Associations are obtained the help of frequencies, prevalence, and mean values, ANOVA test, Chi-square, and binary logistic regression were used for the analysis. Results: The mean age of the participants was 63.01±11.12 years. Males were 144 (42.2%) and females were 197 (57.8%) of the study population. It was found that the self-assessment is influenced by education level of participant (p=0.003) and time elapsed since diagnosis of hypertension (p<0.001). Majority of the participants from age group of 34 to 48 and 49 to 63 had good adherence to exercise, that is, 70.4% (19) and 74.6% (100), respectively, (p=0.001). Participants with awareness regarding risk factors and complications of hypertension showed good adherence to exercise. The patients, who adhered to exercise, also consumed less quantity of salt. Conclusion: Instilling positivity in mind of the patient regarding outcome of treatment and lifestyle modifications can help in controlling the high blood pressure and there by prevent cardiovascular and renal disease in the whole population