Spencerytter1165

The characteristics of the thermal field in the human nasal cavity during the expiration period were investigated using computational fluid dynamics. Heat and water-vapor recovery features were quantitatively investigated under realistic distributions of the epithelial surface and air temperature. A constant expiratory flow rate of 250 mL/s was assumed. The epithelial surface temperature was approximately 34.3-34.4 °C in the nasopharynx and 33.5-33.6 °C in the vestibule region, and these values are in good agreement with the measurement data in the literature. We observed that heat-recovery from the exhaled air mostly occurred in the posterior turbinate region, and the amount of heat recovered is estimated to be approximately 1/3 of the heat supply during inspiration. Because of this heat transfer from the exhaled air to the epithelial surface, the temperature of the epithelial surface increased in this region, and the exhaled air temperature dropped through the turbinate airway. Water-vapor recovery primarily occurs in the posterior segments of the turbinates; however, the amount of water-vapor transfer was approximately 1/5 of that in inspiration. Accordingly, the relative humidity of the exhaled air remained constant throughout the airway.Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.Deep learning has emerged as a leading machine learning tool in object detection and has attracted attention with its achievements in progressing medical image analysis. Convolutional Neural Networks (CNNs) are the most preferred method of deep learning algorithms for this purpose and they have an essential role in the detection and potential early diagnosis of colon cancer. In this article, we hope to bring a perspective to progress in this area by reviewing deep learning practices for colon cancer analysis. This study first presents an overview of popular deep learning architectures used in colon cancer analysis. After that, all studies related to colon cancer analysis are collected under the field of colon cancer and deep learning, then they are divided into five categories that are detection, classification, segmentation, survival prediction, and inflammatory bowel diseases. Then, the studies collected under each category are summarized in detail and listed. #link# We conclude our work with a summary of recent deep learning practices for colon cancer analysis, a critical discussion of the challenges faced, and suggestions for future research. This study differs from other studies by including 135 recent academic papers, separating colon cancer into five different classes, and providing a comprehensive structure. We hope that this study is beneficial to researchers interested in using deep learning techniques for the diagnosis of colon cancer.

To analyse the feasibility of directional ablation using a multi-tine electrode.

A multi-tine electrode capable of operating in multipolar mode has been used to study the directional ablation. In addition to the basic design, similar to commercially available FDA approved multi-tine electrode, tines have been insulated from each other inside the probe base and tip using a thin insulating material of thickness 0.25mm. A cylindrical single-compartment model of size 6cm×6cm has been used to model normal liver tissue. The temperature-controlled radiofrequency ablation has been employed to maintain the tine-tips at different temperatures. Electro-thermal simulations have been performed by using a commercial multi-physics software package based on finite element methods. To make this study feasible a new approach to predict the ablations have been proposed and used in this study.

EI1 solubility dmso with up to 5mm difference in ablation boundary between the intended and non-intended direction has been observed along the transverse direction. Reduction in ablation up to 5mm along the axial direction in comparison to the monopolar mode has also been observed.

Multi-tine electrode modified to operate in multipolar mode can create directional ablations of different shapes and can be used to target position and shape specific tumours.

Multi-tine electrode modified to operate in multipolar mode can create directional ablations of different shapes and can be used to target position and shape specific tumours.