We have used incremental stress-strain curves to study the mechanical behavior of porcine aorta, carotid artery, and vena cava. Elastic and viscous stress-strain curves are composed of low and high strain regions that are approximately linear. Analysis of the low strain behavior is consistent with previous studies that suggest that the behavior is dominated by the behavior of elastic fibers, and that the collagen and elastic fibers are in parallel networks. At high strain, the behavior is different than that of skin where it is dominated by the behavior of the collagen fibers. The high strain behavior is consistent with a series arrangement of the collagen and smooth muscle; however, the arrangement of smooth muscle and collagen may be different in aorta than in the other vessels studied. It is concluded that the mechanical behavior of the vessel wall differs from the behavior of other extracellular matrices that do not contain smooth muscle. Our results indicate that at least some of the collagen fibrils in the media are in series with smooth muscle cells and this collagen-smooth muscle network is in parallel with parallel networks of collagen and elastic tissue in aorta, carotid artery, and vena cava. It is concluded that the series arrangement of collagen and smooth muscle may be important in mechanochemical transduction in vessel walls and that the exact quantity and arrangement of these components may differ in different vessels.

The aortic wall contains collagen fibrils, smooth muscle cells, and elastic fibers as the primary load-bearing components. It is well known that the collagen fibrils bear loads in the circumferential direction, whereas elastic fibers provide longitudinal as well as circumferential support. Stiffening of the vessel wall is associated with loss of elastic tissue and increases in the collagen content: however, little is known about the mechanism of vessel wall stiffening with age. The purpose of this review is to attempt to relate structural changes that occur to the collagen and elastic fibers to changes in the viscoelastic behavior that are associated with aging. Analysis of the viscoelastic mechanical properties of collagen fibrils from tendon, skin, and aortic wall suggest that the collagen fibrils of aortic wall are different than those of other tissues. The elastic spring constant of the collacen fibrils in vessel walls is significantly less than that found in tendon, suggesting that the presence of type III collagen in aortic wall increases the flexibility of the collagen fibrils. Furthermore, it is hypothesized that changes in the interface between collagen fibrils, elastic fibers, and smooth muscle during aging and in connective tissue disorders leads to changes in the viscoelasticity of the vessel wall.

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in

Object-based segmentation is a challenging topic. Most of the previous algorithms focused on segmenting a single or a small set of objects. In this paper, the multiple class object-based segmentation is achieved using the appearance and bag of keypoints models integrated over meanshift patches. We also propose a novel affine invariant descriptor to model the spatial relationship of keypoints and apply the Elliptical Fourier Descriptor to describe the global shapes. The algorithm is computationally efficient and has been tested for three real datasets using less training samples. Our algorithm provides better results than other studies reported in the literature.

BACKGROUND-Autophagy is a starvation induced cellular process of self-digestion that allows cells to degrade cytoplasmic contents. The understanding of autophagy, as either a mechanism of resistance to therapies that induce metabolic stress, or as a means to cell death, is rapidly expanding and supportive of a new paradigm of therapeutic starvation.

One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L2E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L2E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.

Histopathology images are crucial to the study of complex diseases such as cancer. The histologic characteristics of nuclei play a key role in disease diagnosis, prognosis and analysis. In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. Our CAE is the first unsupervised detection network for computer vision applications. The pretrained nucleus detection and feature extraction modules in our CAE can be finetuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and reduce the errors of state-of-the-art methods up to 42%. We are able to achieve comparable performance with only 5% of the fullysupervised annotation cost.

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