Osteoporosis is a metabolic bone disorder characterized by low bone mass, degradation of bone micro-architecture and susceptibility to fracture. It is a growing major health concern across the world, especially in the elderly population. Osteoporosis can cause hip or spinal fractures that may lead to high morbidity and socio-economic burden. Therefore, there is a need for early diagnosis of osteoporosis and prediction of fragility fracture risk. In this paper, state-of-the-art and recent advances in imaging techniques for diagnosis of osteoporosis and fracture risk assessment have been explored. A review of segmentation methods used to segment the regions of interest and texture analysis methods used for classification of healthy and osteoporotic subjects are also presented. Furthermore, challenges posed by the current diagnostic tools have been studied and feasible solutions to circumvent the limitations are discussed. Early diagnosis of osteoporosis and prediction of fracture risk requires the development of highly precise and accurate low-cost diagnostic techniques that would help the elderly population in low economies.

Segmentation of lesions is crucial for the detection and treatment of ischemic stroke. The aim of this work is to develop a robust and highly accurate framework for lesion stroke detection in brain Magnetic Resonance Imaging (MRI). The paper propose a novel deep learning model, named EnigmaNet, for the segmentation of ischemic stroke lesions in Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion Weighted Imaging (DWI) images. EnigmaNet use novel Genesis-k blocks and dual attention mechanism in the encoder and decoder blocks of the architecture. A modified Weighted Focal-Tversky-Dice (wFTD) Loss is used for improved performance. The model is validated on the ISLES-2015 public dataset. EnigmaNet showed a Dice score of 0.8965, sensitivity of 0.8776 and specificity of 0.9866 for the FLAIR test images. Dice score of 0.8423, sensitivity of 0.8452 and specificity of 0.9754 were obtained for DWI images. The segmentation results of EnigmaNet shows an improvement in Dice score of about 32% over U-Net-sharp, 41% over FCN-8 and 10% over Attention U-Net. A region-based comparison of segmentation results of EnigmaNet highlighted its ability to detect fine lesions accurately in multiple vascular territories in brain, thereby signifying its robustness to segment lesions of diverse size, shape and location. The proposed model showed improved results as compared to the state-of-the-art techniques. EnigmaNet model is thus a promising approach for accurate and robust segmentation of ischemic stroke lesions.

Automatic segmentation of vertebrae from computed tomography (CT) scans play an important role in the clinical interpretation and treatment of spinal co-morbidities. Labelling and segmentation of vertebrae is labour intensive and challenging, due to various fields of view and fuzzy boundaries in CT scans. Therefore, successful labelling and segmentation is highly dependent on the level of expertise of the radiologist. In this paper, we propose a three-step fully-automated end-to-end pipeline for vertebra labelling and segmentation of spinal CT images. A novel deep learning architecture, Unbalanced-UNet, is proposed for extracting the region proposals for spine detection. A modified SpatialConfiguration-Net, 3D SCN, is used for labelling of vertebra and centroid extraction. Finally, a 3D U-Net is employed for the segmentation of each vertebra. The models were validated on VERSE’19 public dataset. An identification rate of 90.20% and 91.47% was obtained for the first and second test sets of the VERSE’19 dataset, respectively. Mean localization distance of 4.97 mm and 5.32 mm was obtained for the first and second test sets, respectively. The final segmentation stage shows a dice score and Hausdorff surface distance of 93.07% and 5.36 mm, respectively, for the first test set, and 92.01% and 5.63 mm, respectively, for the second test set. The results show that the proposed approach outperforms the state-of-art models for segmentation of vertebrae. The proposed Unbalanced-UNet architecture increased the accuracy of accruing the region proposals for spine detection. The proposed fully automated pipeline has potential clinical applications in treatment and surgical planning of spinal deformities.

Supercapacitors can act as an instant energy source to quickly supply electric power to any connected system because they are energy storage devices with high power densities. This feature of the supercapacitor can be used to reduce power fluctuations and access latency in cache memories in the VLSI (very-large-scale integration) design of quickly operating artificial intelligence (AI) processors and memory chips. High-performance computing with the best data throughput is produced by this high-power stability and instantaneous power delivery. This article reviews various thin film micro-supercapacitor integration techniques, fabrication processes, and electrode and electrolyte materials, applicable for on-chip integration of micro-supercapacitors on AI processors and memory chips. This review extends the discussion of the thin film versions of supercapacitors in planar and vertically stacked configurations and their relative advantages in mediating ionic conduction. On-chip fabrication of micro-supercapacitor by laser micropatterning, laser surface roughening, carbonization by pyrolysis, laser-induced reduction, and carbon MEMS to scribe electrode patterns with different materials are reviewed. For the on-chip integration of micro-supercapacitors, this examines various fabrication techniques including photolithography, such as monolithic integration, heterogeneous integration, and 3D stacking. The synthesis and implementation of various carbon-based, transition metal oxide-based, and conducting polymer-based electrode materials in both their pure and composite forms are reviewed.