Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis

Curvature-induced stiffening of a fish fin

How fish modulate their fin stiffness during locomotive manoeuvres remains unknown. We show that changing the fin's curvature modulates its stiffness. Modelling the fin as bendable bony rays held together by a membrane, we deduce that fin curvature is manifested as a misalignment of the principal bending axes between neighbouring rays. An external force causes neighbouring rays to bend and splay apart, and thus stretches the membrane. This coupling between bending the rays and stretching the membrane underlies the increase in stiffness. Using analysis of a 3D reconstruction of a Mackerel (Scomber japonicus) pectoral fin, we calculate the range of stiffnesses this fin is expected to span by changing curvature. The 3D reconstruction shows that, even in its geometrically flat state, a functional curvature is embedded within the fin microstructure owing to the morphology of individual rays. Since the ability of a propulsive surface to transmit force to the surrounding fluid is limited by its stiffness, the fin curvature controls the coupling between the fish and its surrounding fluid. Thereby, our results provide mechanical underpinnings and morphological predictions for the hypothesis that the spanned range of fin stiffnesses correlates with the behaviour and the ecological niche of the fish

A study of change in human trabecular bone structure with age and during osteoporosis

The objective of this work was to develop new techniques to view trabecular bone three-dimensionally, and to study its structure and the changes that occur with age and in osteoporosis; the methods used included 3D methods in the SEM, laser confocal microscopy, pseudo-holograms and a "continuous motion parallax method". A detailed analysis of trabecular bone from fourth lumbar vertebral bodies used macro-stereophotographs produced by tilting a sample 10°. Models are proposed for both normal and osteoporotic architecture. A quantitative analysis of the lengths of horizontally oriented trabeculae was carried out. A significant decrease in the number of both vertically and horizontally oriented trabeculae was found. The importance of the influence of different developmental patterns on the formation of the normal structure and of the changing vascularisation on osteoporotic structure are emphasised. Two-dimensional fast Fourier transform methods were employed to study changes in the spatial frequency of trabeculae as a function of orientation. A decrease in spatial frequency was observed in both sexes, but in males this was evident only after the mid-sixth decade in the limited sample studied. Contoured power spectra discriminated different trabecular patterns and the intensity mapping of optical density provided volume density information. Templated reverse transformation was used to study individual orientations of trabeculae. Changes in the quality of trabecular bone with age were also investigated using techniques that analyse bone before and after removal of unmineralised matrix. All specimens were less stiff after removal of osteoid; this was more marked in older specimens. Locally defective mineralisation would explain the changed behaviour observed in some old and osteoporotic specimens. Trabecular fracture patterns had a strong relationship to architecture and microstructure. Scanning electron microscopy was used to study trabecular surfaces. An uncoupling between resorption and formation was evident in older specimens. Two resorption patterns responsible for thinning and perforation and removal trabecular elements were identified. Trabecular microfractures were also investigated

Advances in the Role of Quantitative NMR in Medicine: Deep Learning applied to MR Fingerprinting and Trabecular Bone Volume Fraction Estimation through Single-Sided NMR

Nuclear Magnetic Resonance (NMR) has been a powerful and widespread tool since its birth thanks to its flexibility in assessing properties of physical systems without being invasive and without using ionizing radiations. Although applications of NMR for medical purposes have rapidly developed since the introduction of MR imaging (MRI), most of the clinical protocols retrieve qualitative information about biological tissues. Being able to retrieve also quantitative information with NMR may be beneficial to identify biomarkers for understanding and describing the pathophysiology of complex diseases in many tissues. However, established quantitative MRI (qMRI) methods require long scan times that not only can represent more exposure to image artifacts and more discomfort for the patient, but they also increase the costs of MRI protocols. To improve the clinical feasibility of quantitative NMR, one can focus on optimizing qMRI protocols to increase data acquisition efficiency, i.e. minimizing the acquisition times and maximising the number of retrieved information. Alternatively, one can focus on the application of low-cost, portable and low maintenance NMR devices in the medical field, such as single-sided devices. This Ph.D thesis presents studies that aim to advance the role of quantitative NMR in medicine using the two directions stated above. The first part of the thesis proposes a deep learning approach based on deep Fully Connected Networks (NN), for pixel-wise MR parameter prediction task in Magnetic Resonance Fingerprinting (MRF) as a solution to overcome the curse of dimensionality affecting the gold standard dictionary approach. The second part proposes a methodology to assess the trabecular bone-volume-to-total-volume (BV/TV) ratio using single-side NMR by means of NMR relaxometry measurements. Nowadays there are not well-established methodologies to assess trabecular BV/TV that are suitable for wide screening campaigns of the population at risk of bone fractures related to diseases such as osteoporosis

Model-based segmentation and registration of multimodal medical images

Ph.DDOCTOR OF PHILOSOPH

ST-V-Net: Incorporating Shape Prior Into Convolutional Neural Netwoks For Proximal Femur Segmentation

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance

An investigation of techniques in deformable object recognition

The human\u27s innate ability to process information garnered from a visual scene has no parallel in the digital realm. This task is taken for granted in human cognition, but has not been met by a complete digital solution even following years of research. This difficulty can be explained by the shear complexity of the physology of the visual pathway. Although a complete solution has not been created, there are a number of examples of solutions that address parts of the problem. The recognition of deformable objects is the area addressed in this work. The specific task researched was the recognition of creatures in structured visual scenes. The focus was on developing a set of features which are able to differentiate between target creature classes. The implications of this research lie in ecoinformatics and field biology with the automated collection and annotation of biological data. The thesis will present a survey of the current literature addressing techniques which have been used to solve similar problems. An algorithm to perform the recognition will be presented and the results discussed. Finally, potential areas for improvement will be described