Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

Novel Application Of Untargeted Metabolomics To Diseases Of Neurosurgical Significance

Metabolomics, an emerging technique to study hundreds of small-molecule metabolites simultaneously, has been seldom applied to diseases of neurosurgical significance. We utilized metabolomics to explore two distinct questions: 1. to identify global metabolic changes and metabolite predictors of long-term outcome in aneurysmal subarachnoid hemorrhage (SAH) patients, 2. to identify differential metabolites profiles of radiation necrosis vs. recurrent tumor of metastatic brain lesions post-Gamma Knife radiosurgery. The first study applied gas chromatography time-of-flight mass spectrometry (GC-TOF) to cerebrospinal fluid samples collected from 15 high-grade aSAH patients (modified Fisher grades 3 and 4). Analysis was performed at two time points; metabolite levels at each time point were correlated with Glasgow Outcome Scale (GOS) of patients at 1 year post-aSAH. Of 97 metabolites identified, 16 metabolites (primarily free amino acids) significantly changed between the two time points; these changes were magnified in modified Fisher grade 4 compared with grade 3. Six metabolites (2-hydroxyglutarate, tryptophan, glycine, proline, isoleucine, and alanine) correlated with GOS at 1 year post-aSAH. These results suggest that specific metabolite changes occur in the brain during the course of aSAH and that quantification of specific CSF metabolites may be used to predict long-term outcomes. This is the first study to implicate 2- hydroxyglutarate, a known marker of tissue hypoxia, in aSAH pathogenesis. The second study applied GC- TOF to histologically-validated specimens (7 each) of pure radiation necrosis and pure recurrent tumor obtained from patient brain biopsies. Of 141 metabolites identified, 17 were found to be statistically significantly different between comparison groups. Of these metabolites, 6 were increased in tumor, and 11 metabolites were increased in radiation necrosis. An unsupervised hierarchical clustering analysis found that tumor had elevated levels of metabolites associated with energy metabolism whereas radiation necrosis had elevated levels of metabolites that were fatty acids and antioxidants/cofactors. This is the first tissue- based metabolomics study of radiation necrosis and tumor. Radiation necrosis and recurrent tumor following Gamma Knife radiosurgery for brain metastases have unique metabolite profiles that may be targeted in the future to develop non-invasive metabolic imaging techniques

Improving Attenuation Correction in Hybrid Positron Emission Tomography

Hybrid positron emission tomography imaging techniques such as PET/CT and PET/MR have undergone significant developments over the last two decades and have played increasingly more important roles both in research and in the clinic. A unique advantage PET has over other clinical imaging modalities is its capability of accurate quantification. However, as the most critical component of PET quantification, attenuation correction in hybrid PET systems is challenged in several different aspects, including the spatial- temporal mismatch between the PET emission images and the associated attenuation images provided by the complementary modality, and the difficulty in bone identification in the MR-based attenuation correction approaches. These problems, if left unaddressed, can limit the potential of the hybrid PET systems. This research developed solutions to overcome the spatial-temporal mismatch in PET/CT and PET/MR, and established the requirements for bone identification in PET/MR. An automatic registration algorithm based on a modified fuzzy c-means clustering method and gradient correlation was developed and validated to perform automatic registration in cardiac PET/CT data of different breathing protocols. A free- breathing MR protocol and post-process algorithm were developed to provide MR-based attenuation images that also match the temporal resolution of PET and were evaluated in a feasibility study. The relationship between the sensitivity of bone identification in attenuation images and PET quantification of bone lesions uptake was evaluated in a simulated study using data from 18F-sodium fluoride PET/CT exams

Temporal Dynamics Of The Skin Microbiome In Disease

The skin is colonized by communities of bacteria, fungi, and viruses, collectively referred to as the skin microbiome. These microbial communities are shaped by the topology and diseases of the skin. Dysbiosis of the cutaneous microbiome has been associated with several ailments of the skin including atopic dermatitis, acne, rosacea, psoriasis, and chronic wounds. However, our understandings of the processes by which these microbes initiate, maintain, or modulate skin diseases is lacking. Moreover, previous research on the topic has largely been limited by cross-sectional study designs, neglecting the natural dynamism of microbial communities. Here we present a comprehensive analysis of the temporal dynamics of the skin microbiome in various diseases. In the first section, we characterize the diversity and dynamics of both bacterial and fungal communities colonizing chronic wounds and its associations with clinical outcomes. In a study of 100 subjects with diabetic foot ulcers, we sampled the wound microbiota in 2-week intervals until healing, amputation of 26 weeks of follow-up. We demonstrate the high levels of community instability in chronic wounds and expose the positive association between wound healing community instability. We also reveal the effect of antibiotic perturbation on the microbiota. The fungal component was found to have associations with various bacteria and clinical outcomes. Our results should inform the design of future studies and provides evidence that microbial dynamics may be an effective biomarker for identifying high-risk ulcers. The second section investigates the body-site specific effects of psoriasis on the skin microbiome and how it responds to therapy. We reveal these patterns in a study of 114 subjects, across 6 body sites, and over 112 weeks of follow-up. The effect of psoriatic lesions was found to be mild and body-site specific. In contrast, ustekinumab treatment was found to induce moderate shifts in microbial composition, including an increase in atypical skin bacteria and inter-individual heterogeneity. These results suggest that the effect of psoriasis lesions is secondary to the effect the broad effects of the immune environment. Together the work presented in this thesis represents a significant advancement in our understanding of the microbial dynamics of the skin and their associations with human health