Detection of Neovascularization Based on Fractal and Texture Analysis with Interaction Effects in Diabetic Retinopathy

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.published_or_final_versio

Reference database of total retinal vessel surface area derived from volume-rendered optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) enables three-dimensional, high-resolution, depth-resolved flow to be distinguished from non-vessel tissue signals in the retina. Thus, it enables the quantification of the 3D surface area of the retinal vessel signal. Despite the widespread use of OCTA, no representative spatially rendered reference vessel surface area data are published. In this study, the OCTA vessel surface areas in 203 eyes of 107 healthy participants were measured in the 3D domain. A Generalized Linear Model (GLM) model analysis was performed to investigate the effects of sex, age, spherical equivalent, axial length, and visual acuity on the OCTA vessel surface area. The mean overall vessel surface area was 54.53 mm2 (range from 27.03 to 88.7 mm2). OCTA vessel surface area was slightly negatively correlated with age. However, the GLM model analysis identified axial length as having the strongest effect on OCTA vessel surface area. No significant correlations were found for sex or between left and right eyes. This is the first study to characterize three-dimensional vascular parameters in a population based on OCTA with respect to the vessel surface area

Methods for Data Management in Multi-Centre MRI Studies and Applications to Traumatic Brain Injury

Neuroimaging studies are becoming increasingly bigger, and multi-centre collaborations to collect data under similar protocols, but different scanning sites, are now commonplace.However, with increasing sample size the complexity of databases and the entailed data management as well as computational burden are growing. This thesis aims to highlight and address challenges faced by large multi-centre magnetic resonance imaging(MRI) studies. The methods implemented are then applied to traumatic brain injury (TBI) data.Firstly, a pre-processing pipeline for both anatomical and diffusion MRI was proposed, that allows for a high throughput of MRI scans. After describing the choices for processing tools,the performance of the integrated quality assurance was assessed based on the results from a large multi-centre dataset for TBI. Secondly, the applicability of the pipelines for processing mild TBI (mTBI) data from three sites was shown in a case study. For this, volumetric and diffusion metrics in the acute phase are analysed for their prognostic potential. Further-more, the cohort was examined for longitudinal changes. Thirdly, independent scan-rescan datasets are examined to gain a better understanding of the degree of reproducibility which can be achieved in imaging studies. This involves analysing the robustness of brain parcellations based on structural or diffusion imaging. The effect of using different MRI scanners or imaging protocols was also assessed and discussed. Fourthly, sources of diffusion MRI variability and different approaches to cope with these are reviewed. Using this foundation,state-of-the art methods for diffusion MRI harmonisation were compared against each other using both a benchmark dataset and mTBI cohort. Lastly, a solution to localise brain lesions was proposed. Its implications for lesion analysis, are assessed in the light of an application to a more severe TBI patient cohort, imaged on two different scanners. Furthermore, a lesion matching algorithm was introduced to automatically examine lesion evolution with time post-injury. In summary, this thesis explored different options for MRI data analysis in the context of large multi-centre studies. Different approaches are studied and compared using a number of different MRI datasets, including scan-rescan data across different MRI scanners and imaging protocols. The potential of the optimised solutions was illustrated through applications to TBI data.CENTER-TB

Adaptive Feature Engineering Modeling for Ultrasound Image Classification for Decision Support

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually significantly underrepresented compared to the non-target class. This makes it difficult to train standard classification models like Support Vector Machine (SVM), Decision Trees, and Nearest Neighbor techniques on biomedical datasets because they assume an equal class distribution or an equal misclassification cost. Resampling techniques by either oversampling the minority class or under-sampling the majority class have been proposed to mitigate the class imbalance problem but with minimal success. We propose a method of resolving the class imbalance problem with the design of a novel data-adaptive feature engineering model for extracting, selecting, and transforming textural features into a feature space that is inherently relevant to the application domain. We hypothesize that by maximizing the variance and preserving as much variability in well-engineered features prior to applying a classifier model will boost the differentiation of the thyroid nodules (benign or malignant) through effective model building. Our proposed a hybrid approach of applying Regression and Rule-Based techniques to build our Feature Engineering and a Bayesian Classifier respectively. In the Feature Engineering model, we transformed images pixel intensity values into a high dimensional structured dataset and fitting a regression analysis model to estimate relevant kernel parameters to be applied to the proposed filter method. We adopted an Elastic Net Regularization path to control the maximum log-likelihood estimation of the Regression model. Finally, we applied a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of the thyroid lesion. This is performed to establish the conditional influence on the textural feature to the random factors generated through our feature engineering model and to evaluate the success criterion of our approach. The proposed approach was tested and evaluated on a public dataset obtained from thyroid cancer ultrasound diagnostic data. The analyses of the results showed that the classification performance had a significant improvement overall for accuracy and area under the curve when then proposed feature engineering model was applied to the data. We show that a high performance of 96.00% accuracy with a sensitivity and specificity of 99.64%) and 90.23% respectively was achieved for a filter size of 13 × 13

Towards Patient-Specific Brain Networks Using Functional Magnetic Resonance Imaging

fMRI applications are rare in translational medicine and clinical practice. What can be inferred from a single fMRI scan is often unreliable due to the relative low signal-to-noise ratio compared to other neuroimaging modalities. However, the potential of fMRI is promising. It is one of the few neuroimaging modalities to obtain functional brain organisation of an individual during task engagement and rest. This work extends on current fMRI image processing approaches to obtain robust estimates of functional brain organisation in two resting-state fMRI cohorts. The first cohort comprises of young adults who were born at extremely low gestations and age-matched healthy controls. Group analysis between term- and preterm-born adults revealed differences in functional organisation, which were discovered to be predominantly caused by underlying structural and physiological differences. The second cohort comprises of elderly adults with young onset Alzheimer’s disease and age-matched controls. Their corresponding resting-state fMRI scans are short in scanning time resulting in unreliable spatial estimates with conventional dual regression analysis. This problem was addressed by the development of an ensemble averaging of matrix factorisations approach to compute single subject spatial maps characterised by improved spatial reproducibility compared to maps obtained by dual regression. The approach was extended with a haemodynamic forward model to obtain surrogate neural activations to examine the subject’s task behaviour. This approach applied to two task-fMRI cohorts showed that these surrogate neural activations matched with original task timings in most of the examined fMRI scans but also revealed subjects with task behaviour different than intended by the researcher. It is hoped that both the findings in this work and the novel matrix factorisation approach itself will benefit the fMRI community. To this end, the derived tools are made available online to aid development and validation of methods for resting-state and task fMRI experiments

Neuroimaging in Friedreich's ataxia : new approaches and clinical aplication

Orientadores: Marcondes Cavalcante França Junior, Andreia Vasconcellos FariaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: A ataxia de Friedreich (FRDA) é a ataxia autossômica recessiva mais comum no mundo. Clinicamente, é caracterizada por início precoce, alterações sensoriais e ataxia de lenta progressão. Os estudos de imagem têm focado somente em estruturas infratentoriais, desconsiderando o envolvimento de estruturas supratentoriais, diferenças fenotípicas e duração da doença, bem como a evolução do dano neurológico. Portanto, o objetivo deste trabalho é avaliar, por meio de imagens de ressonância magnética multimodal, pacientes com ataxia de Friedreich a fim de compreender a evolução do dano encefálico, identificar o padrão de dano encefálico entre os fenótipos da doença, os sítios de depósitos de ferro extra-cerebelares e as primeiras estruturas acometidas na doença. A fim de atingir todos os objetivos, foram recrutados 25 pacientes adultos com a forma clássica da doença, 13 pacientes com início tardio e 12 pacientes pediátricos. Para quantificar a gravidade da doença foi utilizada a escala FARS. O dano estrutural de substância cinza e branca foi avaliado via imagens de ressonância magnética ponderadas em T1, T2 e DTI. Para análise de tais imagens foram utilizadas as ferramentas FreeSurfer, T1 MultiAtlas, DTI Multiatlas, SPM, SpineSeg e TBSS. As comparações de grupos revelaram comprometimento microestrutural multifocal na substância branca encefálica na FRDA, com dano extenso nos pedúnculos cerebelares, corpo caloso e tratos piramidais. Encontramos também alterações na substância cinzenta no núcleo denteado do cerebelo, tronco e córtex motor. Nós não identificamos mudanças volumétricas longitudinais, porém análises prospectivas da substância branca identificaram anormalidades microestruturais progressivas no corpo caloso, tratos piramidais e pedúnculos cerebelares superiores após um ano de seguimento. A respeito do estudo comparando o tipo clássico e o tipo tardio (cFRDA vs. LOFA), nós mostramos que ambos os fenótipos possuem um padrão de anormalidades similares, mas não idênticas. Embora sutis, as diferenças estruturais encontradas ajudam a explicar a variabilidade fenotípica entre estas duas apresentações da doença. Por exemplo, o maior dano microestrutural no trato córtico-espinhal no grupo LOFA ajuda a explicar os sinais piramidais mais exuberantes neste grupo. Não fomos capazes de identificar depósitos de ferro cerebrais nos pacientes com FRDA. Neste sentido, tais depósitos ficariam restritos somente ao núcleo denteado do cerebelo. Por fim, fomos capazes de observar que a manifestação inicial da doença, vista em pacientes pediátricos, se concentra na medula espinhal e no pedúnculo cerebelar inferiorAbstract: Friedreich¿s ataxia (FRDA) is the most common autosomal-recessive ataxia worldwide; it is characterized by early onset, sensory abnormalities and slowly progressive ataxia. Besides that, most of neuroimaging studies have been focused only in infratentorial structures of adult patients. Furthermore, studies comparing different phenotypes of disease does not exist. Therefore, the objective of this study is to assess, using multimodal magnetic (MRI) resonance imaging, patients with Friedreich ataxia to better comprehend the progression of brain damage, to identify the pattern of damage across disease phenotypes, to identify areas with abnormal iron deposits in the brain and to characterize the structures initially damaged in early disease stages. To accomplish that, we enrolled 25 adult patients with classical FRDA, 13 patients with late-onset FRDA and 12 pediatric patients. The FARS scale was employed to quantify the disease severity. To assess the structural damage in gray and white matter, we acquired T1-weighted, T2-weighted and DTI images of the brain. To evaluate these images, we used the following tools: FreeSurfer, T1 MultiAtlas, SPM, DTI MultiAtlas, SpineSeg and TBSS. After group comparisons, there was widespread microstructural damage in the cerebral white matter, including cerebellar peduncles, corpus callosum and pyramidal tracts of patients with FRDA. We also found gray matter volumetric reduction in the dentate nuclei of the cerebellum, brainstem and motor cortex. We did not find volumetric reduction over time, but there was progressive white matter microstructural damage in the corpus callosum, pyramidal tracts and superior cerebellar peduncles after 1 year of follow-up. Regarding the disease phenotypes, we found that both classical FRDA and LOFA have similar, but not identical neuroimaging signatures. Although subtle, the structural differences might help to explain the phenotypic differences seen in both conditions. The corticospinal tracts are damaged in both conditions, but more severely in the late-onset FRDA group, which may explain why pyramidal signs are more evident in the latter subgroup. We failed to identify iron deposits in brain regions other than the dentate nuclei of patients with FRDA. Finally, we found that the spinal cord and inferior cerebellar peduncles are the structures compromised in pediatric patients with FRDADoutoradoFisiopatologia MédicaDoutor em Ciências2014/19786-7, 2015/09793-9FAPES