Anatomical variations of the hepatic artery: a closer view of rare unclassified variants

Background: Defining the hepatic artery anatomy is of great importance for both surgeons and radiologists. Michel classification was designed to classify hepatic artery variations. Nevertheless, there are variations that do not fit into this classification. In this study, we aim to define the incidence of all variations in a healthy liver donor by reviewing their CT scan with special emphasis on variations that do not fit in any of the Michel classes. Materials and methods: A retrospective analysis of CT scan of donors and potential liver donors who were evaluated by triphasic CT scan. The CT scans were reviewed independently by a radiologist and two transplant surgeons. Cases that did not fit in any of the Michel classes were classified as class 0. Results: Out of 241 donors, 210 were classified within the Michel classification, of which 60.9 % were class I and 9.1% class II. Thirty-one donors (12.9%) classified as class 0. Of which, nine, three, two and three had replaced right hepatic artery from pancreaticoduodenal artery, gastroduodenal artery, aorta and celiac artery, respectively. Two and 6 donors had accessory right hepatic artery from pancreaticoduodenal artery and gastroduodenal artery respectively.  Segment 4 artery originated from left and right hepatic artery in 56.8% and 31.9%, respectively. Conclusions: A great caution should be taken when evaluating the hepatic artery anatomy, clinicians should anticipate and be familiar with the rare unclassified variations of the hepatic artery

Multi-model CNN fusion for sperm morphology analysis

Infertility is a common disorder affecting 20% of couples worldwide. Furthermore, 40% of all cases are related to male infertility. The first step in the determination of male infertility is semen analysis. The morphology, concentration, and motility of sperm are important characteristics evaluated by experts during semen analysis. Most laboratories perform the tests manually. However, manual semen analysis requires much time and is subject to observer variability during the evaluation. Therefore, computer-assisted systems are required. Additionally, to obtain more objective results, a large amount of data is necessary. Deep learning networks, which have become popular in recent years, are used for processing and analysing such quantities of data. Convolutional neural networks (CNNs) are a class of deep learning algorithm that are used extensively for processing and analysing images. In this study, six different CNN models were created for completely automating the morphological classification of sperm images. Additionally, two decision-level fusion techniques namely hard-voting and soft-voting were applied over these CNNs. To evaluate the performance of the proposed approach, three publicly available sperm morphology data sets were used in the experimental tests. For an objective analysis, a cross-validation technique was applied by dividing the data sets into five sub-sets. In addition, various data augmentation scales and mini-batch analysis were employed to obtain the highest classification accuracies. Finally, in the classification, accuracies 90.73%, 85.18% and 71.91% were obtained for the SMIDS, HuSHeM and SCIAN-Morpho data sets, respectively, using the soft-voting based fusion approach over the six created CNN models. The results suggested that the proposed approach could automatically classify as well as achieve high success in three different data sets. © 2021 Elsevier Lt

Numerical Modelling of the Compressional Behaviour of Warp-knitted Spacer Fabrics

WOS: 000272607500012Warp-knitted spacer fabrics are successfully introduced in building constructions as a thin sheet component reinforcement for wall panels, exterior siding, roofing tiles, flooring tiles, pressure pipes etc. Their structural advantages support an armature system of highly oriented yarns and easy cement embodiment for the production of a composite. The compression resistance of spacer fabric is a major advantage with respect to the performance and composite manufacturing process. The optimum compression performance of spacer fabrics varies according to the requirements of the application in question. This investigation focused on the prediction of the compression performance by two-scale (micro and macro) mechanical analysis using the Finite Element Method (FEM). Micromechanical analysis of the unit cell of the spacer layer was conducted for the calculation of its compression resistance. The apparent mechanical properties of the outer layers were also evaluated by micromechanical modelling. The respective properties of the outer and spacer layers are introduced in the macromechanical model of the sample for analysis of the complex deformation during simulation of the compression test, thus realising the second stage of modelling. The computational method proposed is evaluated by comparison of the load - displacement curves resulting from the simulation and experimental data of compression. Moreover, the effect of the structural and physical parameters of the sample on the compression resistance was investigated

Nuclear Medicine

Electronic publication ahead of print available at www.dirjournal.org an

Visualisation of cakes differing in oil content with magnetic resonance imaging

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that can visualise samples' interior by using the signal coming from mobile protons. The aim of this study was to examine the effects of oil content and peanut/raisin addition on cake quality and to illustrate the power of MRI in analysis of moisture and oil distribution. For this purpose, MR images were acquired with a spin echo sequence and relaxation times T-1 and T-2, and moisture content and firmness of cakes were measured. High oil cakes (HOC) had higher moisture content and lower firmness than low oil cakes (LOC). However, addition of raisin/peanut did not affect the firmness of cakes significantly. In MR images, HOC cake crumb, owing to its higher oil content, displayed larger signal intensities. Signal acquired from different slices demonstrated an increase in moisture content from crust to centre of the cakes. Peanut and raisin signals were suppressed in fat and water suppression sequences, respectively. Significant correlation between transverse relaxation time (T-2a) and oil content (R-2 = 0.99) was found. Moreover, longitudinal relaxation time (T-1) was found to be strongly correlated with moisture content (R-2 = 0.99). The results demonstrated MRI's power as an accurate and non-invasive analysis method in baking