Python-Based Analysis to Segment Bone and Soft Tissue in a Healing Callus

The main objective of this study was to produce a Python script that would help facilitate the segmentation process of both bone and soft tissue. The proposed script, in tangent with ImageJ and Mimics, was successful in producing viable results when the bone and soft tissue sample was placed near hydroxyapatite (HA) phantoms during the image acquisition process. It was important to acquire both the HA phantoms and the sample within the same image sequence as the script functioned by analyzing the statistical distribution of the different HA regions to locate the most ideal thresholding ranges to determine the bone mineral density (BMD) percent composition. When the sample and HA phantoms were in the same set of images, they were both subject to the same type of noise and attenuation, thus allowing for better results to be produced. The script was successful in processing input images and was able to calculate the overall volume and surface area of both the bone and soft tissue, as well as determining the overall bone mineral density of bone. It was also attempted to process bone and soft tissue samples separate of the HA phantoms, but the results were inconclusive

The Role of Bone Sialoprotein in Periodontal Tissue Development and Bone Repair

Bone development and repair involve complex processes that include interaction between cells and their surrounding matrix. In the body, bone sialoprotein (BSP) expression is up-regulated at the onset of mineralization. BSP is a multifunctional acidic phosphoprotein with collagen-binding, hydroxyapatite nucleating, and integrin recognition (RGD sequence, which is important for cell-attachment and signaling) regions. Mice lacking BSP expression (Bsp-/-), exhibit a bone phenotype with reductions in bone mineral density, bone length, osteoclast activation, and impaired bone healing. This thesis examined the role of BSP in tooth development and also its potential use as a therapeutic reagent for bone repair. MicroCT and histological analysis of Bsp-/-mice revealed significant periodontal tissue breakdown marked by defective acellular cementum formation leading to periodontal ligament detachment, extensive alveolar bone and tooth root resorption, and tooth malocclusion. Substituting hard to soft diet, which minimized applied stress during mastication, did not reduce extent of periodontal tissue breakdown. However, soft diets eliminated the incidence of severe incisor malocclusion and the Bsp-/- mice featured normal body weight, long-bone length, and serum alkaline phosphatase activity, suggesting that tooth dysfunction and malnutrition contribute to growth and skeletal defects previously reported. In the bone repair studies, the effectiveness of BSP-treated nano-hydroxyapatite/poly(ester-urethane) (nHA/PU) scaffolds in promoting bone regeneration was determined using a rat calvarial defect model. Recombinant human bone morphogenetic protein (rhBMP-2), which is a potent growth factor was used as the positive control for repair. Addition of BSP to nHA/PU scaffolds improved cell attachment and differentiation, and the consequent osteogenic mineralization in vitro. In vivo, at 6 weeks, microCT and histological analysis indicated that the rhBMP-2 treated nHA/PU scaffolds promoted significant new bone repair, in which approximately 70% of the defect, based on bone volume to total defect volume, was filled with new bone. In contrast, both BSP-treated nHA/PU scaffolds and non-treated scaffolds resulted in approximately 20% new bone formed. These findings suggest that BSP plays a non-redundant role in cementum formation, likely involved in initiating mineralization on the root surface. The effectiveness of nHA/PU scaffold as a carrier for rhBMP-2 and BSP was verified. However, in our system, BSP by itself was not a potent promoter of bone regeneration in vivo

Bone marrow Fat - A Novel Quantification Method and Potential Clinical Applications

Ageing bone is characterised by increased marrow fat infiltration altering its composition and microstructure, thus predisposing the person to osteoporosis. Yet to date, non-invasive quantifications of marrow fat are limited to special MRI techniques, and clinical studies examining marrow fat in the ageing skeleton are scarce. Thus, the key aims of this thesis are to: · Validate a new non-invasive technique of marrow fat quantification using CT technology · Determine the effects of dietary fatty acids on marrow fat · Measure marrow fat content in different skeletal regions in healthy older men · Determine the effect of exercise and calcium on marrow fat. The imaging techniques employed in our animal and human studies were micro CT (µCT) and quantitative CT (QCT) respectively. All images were analysed with the imaging software Slice O Matic version 4.1 (Tomovision). Regions of interest [ROIs] were Volumes of interests (VOIs) of bone, fat and blood measured in µm3 or mm3. Individual tissue volumes, expressed as percentages of the total marrow volume, and ratios of tissue volumes were also used in the analysis. Global and local thresholds for individual tissue volumes were determined separately for µCT and QCT. Thresholds for µCT were those derived from the initial validation study, whereas those for QCT were based on previous published data. To account for partial volume averaging effects, further manual refinement of threshold ranges were undertaken by inspection of individual pixels and their neighbours. This manual process was carried out for both µCT and QCT to derive local thresholds for use in manual segmentation and computation of volumes. Our validation study showed that quantification of marrow fat using µCT was reliable and accurate compared to the gold standard technique- histology- when reliably defined thresholds were used. Good agreement between tissue volumes measured by histology and those computed by the imaging software was demonstrated. We applied this technique to quantify marrow fat in an animal model of senile osteoporosis, and showed that fatty acids (ω- 3 and ω-6) had dual effects on bone. With QCT studies, we confirmed the age related increase in marrow adiposity, and more significantly, different ratios between fat and bone in common fracture regions. Similarly, exercise affects marrow fat differently in different regions, and there was a trend to statistically significant changes to marrow fat with exercise. In conclusion, this body of work showed that quantification of marrow fat using CT is promising, and has future clinical implications. However, significantly more clinical studies are needed to confirm these findings and refine shortfalls in quantification capabilities

Mapping Trabecular Bone Fabric Tensor by in Vivo Magnetic Resonance Imaging

The mechanical competence of bone depends upon its quantity, structural arrangement, and chemical composition. Assessment of these factors is important for the evaluation of bone integrity, particularly as the skeleton remodels according to external (e.g. mechanical loading) and internal (e.g. hormonal changes) stimuli. Micro magnetic resonance imaging (µMRI) has emerged as a non-invasive and non-ionizing method well-suited for the repeated measurements necessary for monitoring changes in bone integrity. However, in vivo image-based directional dependence of trabecular bone (TB) has not been linked to mechanical competence or fracture risk despite the existence of convincing ex vivo evidence. The objective of this dissertation research was to develop a means of capturing the directional dependence of TB by assessing a fabric tensor on the basis of in vivo µMRI. To accomplish this objective, a novel approach for calculating the TB fabric tensor based on the spatial autocorrelation function was developed and evaluated in the presence of common limitations to in vivo µMRI. Comparisons were made to the standard technique of mean-intercept-length (MIL). Relative to MIL, ACF was identified as computationally faster by over an order of magnitude and more robust within the range of the resolutions and SNRs achievable in vivo. The potential for improved sensitivity afforded by isotropic resolution was also investigated in an improved µMR imaging protocol at 3T. Measures of reproducibility and reliability indicate the potential of images with isotropic resolution to provide enhanced sensitivity to orientation-dependent measures of TB, however overall reproducibility suffered from the sacrifice in SNR. Finally, the image-derived TB fabric tensor was validated through its relationship with TB mechanical competence in specimen and in vivo µMR images. The inclusion of trabecular bone fabric measures significantly improved the bone volume fraction-based prediction of elastic constants calculated by micro-finite element analysis. This research established a method for detecting TB fabric tensor in vivo and identified the directional dependence of TB as an important determinant of TB mechanical competence

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy

Study of deformation and failure mechanisms in the human vertebra

Vertebral fractures, which are fractures of the bone in the spine, are the most common type of fracture in people aged 50 and older. These fractures are strongly associated with impaired quality of life and excess mortality. Accurate estimates of an individual's risk of vertebral fracture are necessary for better treatment and prevention of these fractures, but these estimates have remained elusive. This dissertation project seeks to fill gaps in knowledge that exist regarding a key prerequisite for developing better indicators of fracture risk: assessment of non-invasive methods for predicting deformation and failure mechanisms in the vertebra. The first part of this work focused on improving the performance of image-based finite element (FE) models in predicting the deformation and failure patterns that occur during vertebral fractures. These FE models are built from computed tomography (CT) images of a spine, and thus they are possible to use in a clinical setting for patient-specific predictions of vertebral fracture. Compared with currently used clinical methods, CT-based FE models have the advantage of integrating patient-specific geometry, image-based estimates of material properties, and physiological loading conditions to predict the mechanical behavior of the vertebra. However, recent findings suggest that poor estimation of material properties may be a major cause of prediction errors in these models. The first two studies in this dissertation therefore sought to determine how different constitutive models for bone tissue material properties influence the accuracy of the FE predictions of deformation and failure of the vertebra. One study compared two different yield criteria together with two constitutive relationships based on CT-measured density, and the other study tested the use of a constitutive relationship that accounts for microstructural anisotropy, rather than only density. In both studies, displacement fields throughout whole vertebral body measured experimentally in prior work were used as the ground truth in the calculation of prediction errors. We found that constitutive relationships based only on density are not sufficient when seeking accurate FE predictions of deformation fields in the vertebra. The second part of this work focused on deformation and failure mechanisms in a specific region of the vertebra: the vertebral endplate. The vertebral endplate is a thin, porous platform of mineralized tissue at the top and bottom boundaries of the vertebra where it connects with the intervertebral disc. Clinical observations have revealed that the vertebral fractures frequently occur in the region of the vertebral endplate. However, the mechanical behavior of this region is incompletely understood. We therefore sought to quantify the macroscale and microscale mechanical properties of the vertebral endplate. Four-point bend tests were conducted on specimens of the vertebral endplate to quantify the macroscale mechanical behavior and to determine associations between this behavior and composition. Subsequently, FE modeling of the bend tests and Baysesian optimization were used to identify the microscale material properties of the tissue within the vertebral endplate. We found that the macroscale properties of the vertebral endplate are moderately well predicted by density, including measures of density obtained from high-resolution CT scans. However, we also found wide variations in mechanical properties--in particular fracture strain--that were not associated with any of the measures of density or composition. Taken together, these results of these studies provide important data on the use of non-invasive measures of bone density and microstructure to predict vertebral deformation and failure. We find that, at multiple length scales, measures of density obtained from CT imaging, are only moderately beneficial in understanding and predicting vertebral mechanical behavior. Further work is required to develop and incorporate constitutive models that account for microstruture, not just density. This outcome, together with the quantitative measurements we provide on the accuracy of FE-based predictions of vertebral failure and the mechanical properties of the vertebral endplate, constitute critical milestones along the path towards robust estimation of the risk of vertebral fracture risk in clinical settings

Coronectomy of deeply impacted lower third molar : incidence of outcomes and complications after one year follow-up

Objectives: The purpose of present study was to assess the surgical management of impacted third molar with proximity to the inferior alveolar nerve and complications associated with coronectomy in a series of patients undergoing third molar surgery. Material and Methods: The position of the mandibular canal in relation to the mandibular third molar region and mandibular foramen in the front part of the mandible (i.e., third molar in close proximity to the inferior alveolar nerve [IAN] or not) was identified on panoramic radiographs of patients scheduled for third molar extraction. Results: Close proximity to the IAN was observed in 64 patients (35 females, 29 males) with an impacted mandibular third molar. Coronectomy was performed in these patients. The most common complication was tooth migration away from the mandibular canal (n = 14), followed by root exposure (n = 5). Re-operation to remove the root was performed in cases with periapical infection and root exposure. Conclusions: The results indicate that coronectomy can be considered a reasonable and safe treatment alternative for patients who demonstrate elevated risk for injury to the inferior alveolar nerve with removal of the third molars. Coronectomy did not increase the incidence of damage to the inferior alveolar nerve and would be safer than complete extraction in situations in which the root of the mandibular third molar overlaps or is in close proximity to the mandibular canal

Ensuring the in vitro degradation reproducibility of powder metallurgy processed Mg 0.6Ca system

Magnesium degradation is a complex phenomenon that is too difficult to be described by a single influential parameter. Magnesium degradation is often influenced by either overtaking or overlapping factors like the cell culture medium composition, physiological conditions, impurities, and material’s internal microstructure, etc. This poses a challenge in obtaining the reproducible degradation results. Hence, in the present work, microstructural features like porosity and grain size distributions in powder metallurgy (PM) Mg-0.6Ca system were discretely evaluated for their roles in altering the specimen in vitro degradation rates. Importance was also given to the specimen impurity and mechanical properties. Based on the results, the limitations in PM processing conditions towards obtaining robust degradation results or, in other words, the material parameter thresholds to be realized for obtaining reproducible degradation profiles in PM Mg-0.6Ca specimens were put forth. Additionally, using literature evidence, the mechanisms governing pore closure and grain growth during liquid phase sintering of Mg-0.6Ca specimens from the PM processing perspective were determined. PM Mg-0.6Ca specimens were fabricated via powder blending of pure magnesium and master alloy Mg-10Ca powders. Specimens of seven different porosities, from 3% to 21%, were produced by varying sintering temperatures. Specimens with heterogeneous grain size distributions were obtained by surface modification of pure magnesium powders by means of a mechanical sieving treatment. Degradation profiles were analyzed in vitro using a semi static immersion test for 16 days under physiological conditions of 37 °C, 20% O2, 5% CO2, 95% relative humidity. Dulbecco’s modified Eagle’s medium was used as cell culture medium with Glutamax and 10% fetal bovine serum as supplements. Mechanical properties were determined using micro tensile specimens. The results indicate that low mean degradation rates (MDR 95% to ≤ 45% when falling below this value. Similarly, the pore interconnectivity sharply drops from > 95% to < 10% at this porosity, thereby enhancing the degradation reproducibility. From PM processing perspective, the sintering temperature of 570 °C is proven as beneficial to promote liquid fractions high enough to enhance specimen sinter density. The present work also showed that heterogeneous grain growth is prompted by the reduced oxide pinning effect at the grain boundaries during sintering of PM Mg-0.6Ca specimens. The heterogeneous grain growth additionally induced the formation of eutectic lamellar structure α-Mg + Mg2Ca at certain grain boundaries throughout the microstructure, which is otherwise not evident in specimens with a homogeneous grain size. Based on the literature and results of the present work, it is postulated that this eutectic structure is the major reason for a non-reproducible degradation in PM Mg-0.6Ca specimens possessing a heterogeneous grain structure. Though mechanical properties are not majorly affected, it is recommended that heterogeneous grain growth is to be avoided in PM Mg-0.6Ca specimens. The presented results also implicitly conveyed the flexibility of PM as a viable technique to design Mg-Ca materials with tailor made degradation and mechanical strengths

The validity of two compartment model methods of body composition as compared to magnetic resonance imaging in Asian Indian versus Caucasian males

Background: The two-compartment (2C) model is a relatively accessible, inexpensive and time efficient method for body composition measurement. There is very little validated research on the 2C model in Asian Indians: a high risk population in terms of obesity and related disorders. This highlights the need for valid estimates of body composition from the 2C model. Purpose: The goal was to compare 2C model (predictor) estimates of body composition to those from magnetic resonance imaging (MRI) (criterion), an established gold standard measure of total adiposity in order to determine the validity of the 2C model in the Asian Indian population. From this data it is hoped that a correction equation may be determined for more accurate prediction of Asian Indian body composition using 2C model methods. Methods: 21 males (10 Asian Indian and 11 Caucasian, aged 18-55 yrs) had estimates of percent body fat from 2C methods (sum of four skinfolds and anthropometry, bioelectrical impedance analysis [Bodystat 1500 and Tanita segmental impedance analyser], air displacement plethysmography [Bod Pod] and hydrostatic weighing) compared to MRI measured body composition values. Agreement was assessed using multiple linear regression analysis and Bland-Altman plots. Differences were assessed using repeated measures analysis of variance. Results: Regression analysis showed air displacement plethysmography predicts MRI body composition in Caucasian males (adjusted r2 = 0.74; SEE =3.27 ). In Asian Indians, tricep skinfold thickness and hydrostatic weighing predicted MRI body composition with a low prediction error (adjusted r2 = 0.90; SEE =1.75). Despite strong correlations and no significant difference between mean differences of the 2C methods, used in the prediction model, and MRI, BlandAltman plots revealed no acceptable limits of agreement between the methods. Asian Indian body composition was underestimated by all two compartment devices compared to MRI. Conclusion: There appears to be potential for the use of tricep skinfold thickness and hydrostatic weighing to predict an established reference measure (MRI) in the high risk Asian Indian population. The 2C model underestimated Asian Indian body composition, this suggests that un-validated, the 2C model may misidentify obesity and in turn health risk. However the small sample tested, has implications for the interpretation of the findings. Further investigation is required with a greater sample size to validate the 2C model against an established reference measure such as MRI as there is currently little published validation data in this ethnic group