Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach

This paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. This segmentation approach is compared, using hand-labelled data, to a number of standard segmentation/thresholding methods commonly applied in microscopy. The importance of different features is also assessed using the method of minimum Redundancy, Maximum Relevance (mRMR), and the effect of different SVM kernels is also considered. The results show that the approach suggested in this paper attains much greater accuracy than standard techniques; in our comparisons with manually labelled data, our proposed technique is able to identify boundary pixels to an accuracy of 93%. More significantly, out of a set of 56 regions of image data, 43 regions were binarised to a useful level of accuracy. The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches

Quantitative Analysis of Stenting Effects on Cellular Response

BACKGROUND Generally hypothesized hemodynamic forces and procedures (surgical and stenting) leading to arteriosclerosis and in-stent restenosis still remain entirely unclear. More so, it is difficult to identify and differentiate which of the procedural injury and/or changes in the local hemodynamic forces due to stent presence influence the onset of undesired clinical events. This difficulty in identifying the main factors leading to in-stent restenosis is compounded as procedural injury and change in hemodynamic stresses usually co-exist in-vivo. To simplify the complexity in identifying the predictors of in-stent restenosis, this thesis focused on the effects of local hemodynamic forces within stented artery on endothelial cells that could lead to in-stent restenosis. Endothelial cells (ECs) play a critical element in the maintenance of healthy artery. Experimental studies of endothelial structure and function have presented evidence that physiological hemodynamic forces promote ECs elongation and atheroprotective endothelial phenotype whilst unphysiological hemodynamic forces promote atheroprone and polygonal shaped endothelial. Based on the above stated evidence, an experimental stent-cells interaction flow bio-reactor system was developed. This system is capable of subjecting ECs cultured in vitro to similar hemodynamic forces present within stented arteries in vivo. Computational models have been developed and used as complementary tool in the quantitative analysis of the mechanical forces being applied to the cultured cells. The computational models were validated to guarantee accuracy of computational results. METHODS AND RESULTS Human umbilical vein endothelial cells (HUVECs) were subjected to steady and realistic physiological left anterior descending artery (LAD) flow waveforms at hydrostatic pressures of 120/80 mmHg and 100 mmHg respectively at timescales of 6, 12 and 24 hours within the stent-cell interaction model. The morphology of cells after exposure to the flow conditions where quantified by using a commercial computational image processing programme built on a MATLAB platform. The cells were also labelled for nuclear factor – kappaB (NFkB), a key regulator of inflammatory response and intercellular adhesion molecule (ICAM-1) after being stimulated with 200 U/ml of tumour necrosis factor-alpha (TNF-α) or exposed to the above stipulated flow and pressure conditions. Cultured HUVECs located anterior and proximal to the stented region of the stent-cell interaction model were observed to elongate and align more to the impinged flow direction with increasing time. These regions where marked by uniform wall shear stress (WSSs), spatial wall shear stress gradient (SWSSGs) and negligible oscillatory shear index (OSIs). This observation is consistent with investigations of the morphological changes of endothelial cells subjected to stresses in vivo and in vitro from other researchers. Cells within the stented region however did not show strong alignment to the fluid flow direction. These regions were marked by non-uniform WSSs, SWSSGs and very high OSIs (0.35 – 0.45). Also HUVECs within the stented region were more polygonal shaped. It was also observed that in the absence of fluid stress, hydrostatic pressure stimulated cell proliferation, elongation, random alignment and a formation of cell multi layering structure. The phenomenon of cell multi layering is however absent when there is presence of fluid shear stress. HUVECs stimulated with TNF-α for 1 hour showed very high NF-kB expression whilst those cells exposed to the stipulated combined stress and pressure conditions for the same duration did not show NF-kB expression. Increased levels of ICAM-1 were observed when cells were stimulated with TNF-α for 6, 12 and 24 hours. However cells exposed to stipulated fluid stress and pressure conditions exhibited a time-dependent selective expression of ICAM-1. CONCLUSION It is concluded from results of the experiments performed that different types of combined and/or individual stresses have distinctive effects on HUVECs morphological response and the genes that may be expressed by the cells