MRI image segmentation using machine learning networks and level set approaches

The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones

Influence of Thermal Cycling on Cement Sheath Integrity

The number of well integrity issues increase in the petroleum industry as wells are exposed to severe downhole conditions and have longer lifetimes. Techniques for enhanced oil recovery, like steam injection and field development in the Arctic, expose downhole materials to harsh cyclic temperature variations. This is also the scenario for normal production situations, although not to the same extent. Production can be stopped for various technical or non-technical reasons, or for injection purposes, all of which influence the temperature in a well. Heating and cooling make the steel casing expand and contract as a result of thermal expansion. This volumetric change can influence downhole well barriers, e.g. annular cement sheaths leading them to fail. Failure of annular cement sheaths can introduce well integrity issues and subsequent well leakages of downhole formation fluids. An experimental set-up was build during the present work to investigate the effect of thermal cycling on annular cement sheath integrity. The set-up included all the three materials in a well, casing, cement and rock allowing the whole system to be tested in one assembly at the laboratory scale. The testing specimens are composed of steel pipe cemented in place in a confining rock, thereby representing a downscaled wellbore. Temperature variations were applied radially to the casing and the effect of these variations on cement sheath integrity were observed. In-situ well integrity was continuously monitored by means of Acoustic Emission Testing (AET), and post failure analysis was conducted by Computed Tomography (CT) investigations. Three specimens were tested during the present work: The first sample was not exposed to any thermal cycles ("virgin" sample), and was directly sent to CT investigation after cement curing. The second sample was cemented with a centralized casing and the third was cemented with casing 50% off position. Both of the latter samples were exposed to the same thermal cycle profile.The experimental results from the continuous in-situ AET measurements revealed that casing centralization is important for wellbore integrity and that thinner annular cement sheaths withstand less temperature variations. CT examination and 3D visualization confirmed severe debonding at the casing-cement interface, for all the three specimens, including the uncycled "virgin" specimen. This indicates that the casing-cement bond is generally weak. For the cement-formation interface, the debonding was particularly clear for the two thermally cycled specimens. Furthermore, the 3D visualization results based on CT-scans displayed that, debonding is more prominent than radial cracking. Calculations of interfacial porosity, defined as the volume of interface pores/cracks as a fraction of the total sample volume, revealed that thermal cycling and casing centralization affects the magnitude of debonding and cracking of cement. The "50% casing stand-off" sample showed most interfacial porosity (1.38%), followed by "centralized casing" sample (1.18%) and finally, uncycled "virgin" sample showed, least interfacial porosity (0.59%). This displays that thermal cycling does indeed affect the sealing ability of annular cement sheaths in a negative way.Future work is essential in order to fully understand within which temperature ranges a particular well can be operated, without leaks along the annular cement sheaths. This can be obtained by conducting tests varying the different materials. Experiments with different cement systems, various formations and casing surface finishes can be executed. In addition, experimental tests determining the effect of exposing the formation to drilling fluids prior to cementing and further thermal cycling can be conducted. Effect of various wellbore scaling ratios is also important, as the effect of the material curvatures and total volumes on the obtained results are unknown

Intelligent Control of SMART Materials for Energy Harvesting and Storage Devices

The investigation of innovative materials and intelligent control systems has been motivated by the desire to provide sustainable energy solutions, with the aim of improving the efficiency and adaptability of energy harvesting and storage devices. This study introduces an innovative methodology to tackle this issue by combining SMART (Self-Monitoring, Analysing, and Reporting Technology) materials with sophisticated intelligent control approaches. The system under consideration utilises the intrinsic material characteristics of SMART materials, including piezoelectric, thermoelectric, and shape memory alloys, with the objective of capturing and transforming ambient energy into electrical power that can be effectively utilised. In order to fully harness the capabilities of SMART materials, a novel control framework is proposed that integrates machine learning algorithms, real-time sensor data, and adaptive control procedures. The intelligent control system enhances the effectiveness and durability of energy harvesting and storage devices by effectively adjusting to different operational situations and optimising energy conversion and storage processes. The findings demonstrate significant enhancements in energy conversion efficiency as well as notable advancements in the longevity and dependability of energy systems utilising SMART materials. Furthermore, the capacity of the control system to adjust to various environmental circumstances and energy sources situates this research at the forefront of cutting-edge energy technology

SMART Materials for Biomedical Applications: Advancements and Challenges

The advancement of SMART (Self-Healing, Multifunctional, Adaptive, Responsive, and Tunable) materials has had a significant impact on the domain of biomedical applications. These materials possess distinct characteristics that exhibit responsiveness to alterations in their surroundings, rendering them exceedingly appealing for a wide range of therapeutic applications. This study aims to examine the progress and obstacles related to SMART materials within the field of biomedicine. In recent decades, notable advancements have been achieved in the development, synthesis, and analysis of intelligent materials specifically designed for biomedical purposes. Self-healing materials have been employed in the development of implants, wound healing scaffolds, and drug delivery systems, drawing inspiration from natural regeneration mechanisms. The ongoing advancements in SMART materials have significant opportunities for transforming biological applications. The progression of nanotechnology, biomaterials, and bioengineering is expected to play a significant role in the advancement of materials that possess enhanced qualities and capabilities. The integration of SMART materials with emerging technologies such as 3D printing, gene editing, and microfluidics has the potential to create novel opportunities in the field of precision medicine and personalised healthcare. The effective translation of SMART materials from the laboratory to the clinic will need concerted efforts by researchers, physicians, regulatory agencies, and industry partners to address the present difficulties