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

Abstract

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.

Growth of Human Colon Cancer Cells in Nude Mice is Delayed by Ketogenic Diet With or Without Omega-3 Fatty Acids and Medium-chain Triglycerides

“…Cancer patients with advanced incurable cancer are typically threatened by cancer cachexia, characterised by a negative protein and energy balance driven by a variable combination of reduced food intake and abnormal metabolism , which leads to progressive weight loss, mainly due to loss of skeletal muscle mass (with or without loss of fat mass), and anorexia (Giordano et al, 2005;Fearon et al, 2011). Although cancer cachexia accounts for about 20% of cancer deaths, its underlying mechanisms are not known in detail (Iftikhar et al, 2011;Mondello et al, 2014). To improve the quality of life and survival time of incurable patients, it is important to avert the onset of cachexia.…”

Haemodynamic Wall Shear Stress, Endothelial Permeability and Atherosclerosis—A Triad of Controversy

“…First, most studies showing that elongation depends on WSS have examined cellular rather than nuclear morphology. Nevertheless, the study of Bond et al (2011) , just described, did include validation against a smaller series of measurements concerning elongation of the whole cell; a good correlation was obtained with nuclear elongation regardless of whether the cell outline was analysed manually or by using an automated technique based on machine learning ( Iftikhar et al, 2011 ).…”

Artificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey

“…AI techniques, mainly taken from the machine learning library, have been applied for image segmentation of CT scan Digital Imaging and Communications (DICOM) file formats. A new method that utilizes support vector machines (SVM) has recently been used as the basis for segmenting an image and subsequently detecting the thin boundaries of silver-stained endothelial cells [10]. Results exhibited detection accuracy of 93% for this approach.…”