Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration of medical images, asymmetric analysis, and alignment or tilt correction (recenter and reorientation) in brain MRIs. The parameters of MSP are estimated in two steps. In the first step, symmetric features and principal component analysis (PCA)-based technique is used to vertically align the bilateral symmetric axis of the brain. In the second step, PCA is used to achieve a set of parallel lines (principal axes) from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression. The developed algorithm has been tested on 157 real T 1 -weighted brain MRI datasets including 14 cases from the patients with brain tumors. The presented algorithm is compared with a state-of-the-art approach based on bilateral symmetry maximization. Experimental results revealed that the proposed algorithm is fast (<1.04 s per MRI volume) and exhibits superior performance in terms of accuracy and precision (a mean z-distance of 0.336 voxels and a mean angle difference of 0.06).2 of 23 misalignment of brain MRIs. A general phenomenon in brain MRI scanning is that many neuroimaging scanners produce tilted and distorted brain images. The tilt of the head is not always detectable, due to many reasons such as the health conditions, immobility of patients, imprecision of the data calibration systems, and the inexperience of the technicians. Consequently, the slices of the brain MRIs are no more alike within the same orientation, at either the axial or coronal level [15]. Disoriented and misaligned brain MRIs can betray visual inspection and prevalently yield erroneous clinical perception [16]. In summary, assessment of brain MRIs for any anomaly based on cross-referencing of brain hemispheres (left and right), either by a human expert or computer-based software could be affected by false geometrical representation. Consequently, it is essential to correct the tilt and realign the brain MRIs data before further analysis.Manual misalignment correction is extremely time-consuming and laborious to perform on a huge scale. It also demands an urbane knowledge of brain anatomy. Therefore, it is neither sufficient nor efficient. Alignment or tilt correction of brain MRIs is tantamount to realigning the MSP with the center of the image matrix or image coordinate system [14]. If the MSP is computed precisely, the orientation problem of the MRI volume can be resolved. Thus, the tilt of the head volume can be assessed and adjusted. An ideal MSP can be defined as a virtual geometric plane passing through the interhemispheric fissure (IF) [17], about which the three-dimensional (3-D) anatomical structure of the brain (such as the ventricles, anterior/posterior commissures, corpus callosum, thalamus) exhibits maximum bilateral symmetry [18].Previously, ...

Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.

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As non-renewable conventional fossil fuel sources are depleting day by day, researchers are continually finding new ways of producing and utilizing alternative, renewable, and reliable fuels. Due to conventional technologies, the environment has been degraded seriously, which profoundly impacts life on earth. To reduce the emissions caused by running the compression ignition engines, waste cooking oil (WCO) biodiesel is one of the best alternative fuels locally available in all parts of the world. Different study results are reviewed with a clear focus on combustion, performance, and emission characteristics, and the impact on engine durability. Moreover, the environmental and economic impacts are also reviewed in this study. When determining the combustion characteristics of WCO biodiesel, the cylinder peak pressure value increases and the heat release rate and ignition delay period decreases. In performance characteristics, brake-specific fuel consumption increases while brake-specific energy consumption, brake power, and torque decrease. WCO biodiesel cuts down the emissions value by 85% due to decreased hydrocarbon, SO2, CO, and smoke emissions in the exhaust that will effectively save the environment. However, CO2 and NOx generally increase when compared to diesel. The overall economic impact of production on the utilization of this resource is also elaborated. The results show that the use of WCO biodiesel is technically, economically, environmentally, and tribologically appropriate for any diesel engine.