Investigating the impact of supervoxel segmentation for unsupervised abnormal brain asymmetry detection

Several brain disorders are associated with abnormal brain asymmetries (asymmetric anomalies). Several computer-based methods aim to detect such anomalies automatically. Recent advances in this area use automatic unsupervised techniques that extract pairs of symmetric supervoxels in the hemispheres, model normal brain asymmetries for each pair from healthy subjects, and treat outliers as anomalies. Yet, there is no deep understanding of the impact of the supervoxel segmentation quality for abnormal asymmetry detection, especially for small anomalies, nor of the added value of using a specialized model for each supervoxel pair instead of a single global appearance model. We aim to answer these questions by a detailed evaluation of different scenarios for supervoxel segmentation and classification for detecting abnormal brain asymmetries. Experimental results on 3D MR-T1 brain images of stroke patients confirm the importance of high-quality supervoxels fit anomalies and the use of a specific classifier for each supervoxel. Next, we present a refinement of the detection method that reduces the number of false-positive supervoxels, thereby making the detection method easier to use for visual inspection and analysis of the found anomalies.</p

White matter hyperintensities segmentation: a new semi-automated method

White matter hyperintensities (WMH) are brain areas of increased signal on T2-weighted or fluid-attenuated inverse recovery magnetic resonance imaging (MRI) scans. In this study we present a new semi-automated method to measure WMH load that is based on the segmentation of the intensity histogram of fluid-attenuated inversion recovery images. Thirty patients with mild cognitive impairment with variable WMH load were enrolled. The semi-automated WMH segmentation included removal of non-brain tissue, spatial normalization, removal of cerebellum and brain stem, spatial filtering, thresholding to segment probable WMH, manual editing for correction of false positives and negatives, generation of WMH map, and volumetric estimation of the WMH load. Accuracy was quantitatively evaluated by comparing semi-automated and manual WMH segmentations performed by two independent raters. Differences between the two procedures were assessed using Student’s t-tests and similarity was evaluated using linear regression model and Dice similarity coefficient (DSC). The volumes of the manual and semi-automated segmentations did not statistically differ (t-value = -1.79, DF = 29, p = 0.839 for rater 1; t-value = 1.113, DF = 29, p = 0.2749 for rater 2), were highly correlated [R(2) = 0.921, F((1,29)) = 155.54, p < 0.0001 for rater 1; R(2) = 0.935, F((1,29)) = 402.709, p < 0.0001 for rater 2] and showed a very strong spatial similarity (mean DSC = 0.78, for rater 1 and 0.77 for rater 2). In conclusion, our semi-automated method to measure the load of WMH is highly reliable and could represent a good tool that could be easily implemented in routinely neuroimaging analyses to map clinical consequences of WMH

Multicentre evaluation of MRI variability in the quantification of infarct size in experimental focal cerebral ischaemia

Ischaemic stroke is a leading cause of death and disability in the developed world. Despite that considerable advances in experimental research enabled understanding of the pathophysiology of the disease and identified hundreds of potential neuroprotective drugs for treatment, no such drug has shown efficacy in humans. The failure in the translation from bench to bedside has been partially attributed to the poor quality and rigour of animal studies. Recently, it has been suggested that multicentre animal studies imitating the design of randomised clinical trials could improve the translation of experimental research. Magnetic resonance imaging (MRI) could be pivotal in such studies due to its non-invasive nature and its high sensitivity to ischaemic lesions, but its accuracy and concordance across centres has not yet been evaluated. This thesis focussed on the use of MRI for the assessment of late infarct size, the primary outcome used in stroke models. Initially, a systematic review revealed that a plethora of imaging protocols and data analysis methods are used for this purpose. Using meta-analysis techniques, it was determined that T2-weighted imaging (T2WI) was best correlated with gold standard histology for the measurement of infarctbased treatment effects. Then, geometric accuracy in six different preclinical MRI scanners was assessed using structural phantoms and automated data analysis tools developed in-house. It was found that geometric accuracy varies between scanners, particularly when centre-specific T2WI protocols are used instead of a standardised protocol, though longitudinal stability over six months is high. Finally, a simulation study suggested that the measured geometric errors and the different protocols are sufficient to render infarct volumes and related group comparisons across centres incomparable. The variability increases when both factors are taken into account and when infarct volume is expressed as a relative estimate. Data in this study were analysed using a custom-made semi-automated tool that was faster and more reliable in repeated analyses than manual analysis. Findings of this thesis support the implementation of standardised methods for the assessment and optimisation of geometric accuracy in MRI scanners, as well as image acquisition and analysis of in vivo data for the measurement of infarct size in multicentre animal studies. Tools and techniques developed as part of the thesis show great promise in the analysis of phantom and in vivo data and could be a step towards this endeavour

Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, and Grading

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology (DP) images. Due to uncontrolled growth and infiltration into the surrounding tissues, the tumor regions appear with a significant texture variation in the static MRI volume and also in the longitudinal imaging study. Consequently, this study developed computational models using novel texture features to segment abnormal brain tissues (tumor, and stroke lesions), tracking the change of tumor volume in longitudinal images, and tumor grading in MR images. Manual delineation and analysis of these abnormal tissues in large scale is tedious, error-prone, and often suffers from inter-observer variability. Therefore, efficient computational models for robust segmentation of different abnormal tissues is required to support the diagnosis and analysis processes. In this study, brain tissues are characterized with novel computational modeling of multi-fractal texture features for multi-class brain tumor tissue segmentation (BTS) and extend the method for ischemic stroke lesions in MRI. The robustness of the proposed segmentation methods is evaluated using a huge amount of private and public domain clinical data that offers competitive performance when compared with that of the state-of-the-art methods. Further, I analyze the dynamic texture behavior of tumor volume in longitudinal imaging and develop post-processing frame-work using three-dimensional (3D) texture features. These post-processing methods are shown to reduce the false positives in the BTS results and improve the overall segmentation result in longitudinal imaging. Furthermore, using this improved segmentation results the change of tumor volume has been quantified in three types such as stable, progress, and shrinkage as observed by the volumetric changes of different tumor tissues in longitudinal images. This study also investigates a novel non-invasive glioma grading, for the first time in literature, that uses structural MRI only. Such non-invasive glioma grading may be useful before an invasive biopsy is recommended. This study further developed an automatic glioma grading scheme using the invasive cell nuclei morphology in DP images for cross-validation with the same patients. In summary, the texture-based computational models proposed in this study are expected to facilitate the clinical management of patients with the brain tumors and strokes by automating large scale imaging data analysis, reducing human error, inter-observer variability, and producing repeatable brain tumor quantitation and grading

Development and application of novel algorithms for quantitative analysis of magnetic resonance imaging in multiple sclerosis.

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