Robust whole-brain segmentation: Application to traumatic brain injury

We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.This work was partially funded under the 7th Framework Programme by the European Commission (http://cordis.europa.eu/ist/, TBIcare: http://www.tbicare.eu/, last accessed: 8 December 2014). The research was further supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust and Imperial College London. AH is supported by the Department of Health via the NIHR comprehensive BRC award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London and Kings College Hospital NHS Foundation Trust. This work was further supported by a Medical Research Council (UK) Program Grant (Acute brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [G9439390 ID 65883]), the UK National Institute of Health Research Biomedical Research Centre at Cambridge, the Technology Platform funding provided by the UK Department of Health and an EPSRC Pathways to Impact award. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by an NIHR Senior Investigator Award. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The funders had no role in study design, data collection and analyses, decision to publish, or preparation of the manuscript

Assessment and optimisation of MRI measures of atrophy as potential markers of disease progression in multiple sclerosis

There is a need for sensitive measures of disease progression in multiple sclerosis (MS) to monitor treatment effects and understand disease evolution. MRI measures of brain atrophy have been proposed for this purpose. This thesis investigates a number of measurement techniques to assess their relative ability to monitor disease progression in clinically isolated syndromes (CIS) and early relapsing remitting MS (RRMS). Presented, is work demonstrating that measurement techniques and MR acquisitions can be optimised to give small but significant improvements in measurement sensitivity and precision, which provided greater statistical power. Direct comparison of numerous techniques demonstrated significant differences between them. Atrophy measurements from SIENA and the BBSI (registration-based techniques) were significantly more precise than segmentation and subtraction of brain volumes, although larger percentage losses were observed in grey matter fraction. Ventricular enlargement (VE) gave similar statistical power and these techniques were robust and reliable; scan-rescan measurement error was <0.01% of brain volume for BBSI and SIENA and <0.04ml for VE. Annual atrophy rates (using SIENA) were -0.78% in RRMS and -0.52% in CIS patients who progressed to MS, which were significantly greater than the rate observed in controls (-0.07%). Sample size calculations for future trials of disease-modifying treatments in RRMS, using brain atrophy as an outcome measure, are described. For SIENA, the BBSI and VE respectively, an estimated 123, 157 and 140 patients per treatment arm respectively would be required to show a 30% slowing of atrophy rate over two years. In CIS subjects brain atrophy rate was a significant prognostic factor, independent of T2 MRI lesions at baseline, for development of MS by five year follow-up. It was also the most significant MR predictor of disability in RRMS subjects. Cognitive assessment of RRMS patients at five year follow-up is described, and brain atrophy rate was a significant predictor of overall cognitive performance, and more specifically, of performance in tests of memory. The work in this thesis has identified methods for sensitively measuring progressive brain atrophy in MS. It has shown that brain atrophy changes in early MS are related to early clinical evolution, providing complementary information to clinical assessment that could be utilised to monitor disease progression

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Artificial Intelligence with Light Supervision: Application to Neuroimaging

Recent developments in artificial intelligence research have resulted in tremendous success in computer vision, natural language processing and medical imaging tasks, often reaching human or superhuman performance. In this thesis, I further developed artificial intelligence methods based on convolutional neural networks with a special focus on the automated analysis of brain magnetic resonance imaging scans (MRI). I showed that efficient artificial intelligence systems can be created using only minimal supervision, by reducing the quantity and quality of annotations used for training. I applied those methods to the automated assessment of the burden of enlarged perivascular spaces, brain structural changes that may be related to dementia, stroke, mult

Neuroimaging Evidence of Major Morpho-Anatomical and Functional Abnormalities in the BTBR T+TF/J Mouse Model of Autism

BTBR T+tf/J (BTBR) mice display prominent behavioural deficits analogous to the defining symptoms of autism, a feature that has prompted a widespread use of the model in preclinical autism research. Because neuro-behavioural traits are described with respect to reference populations, multiple investigators have examined and described the behaviour of BTBR mice against that exhibited by C57BL/6J (B6), a mouse line characterised by high sociability and low self-grooming. In an attempt to probe the translational relevance of this comparison for autism research, we used Magnetic Resonance Imaging (MRI) to map in both strain multiple morpho-anatomical and functional neuroimaging readouts that have been extensively used in patient populations. Diffusion tensor tractography confirmed previous reports of callosal agenesis and lack of hippocampal commissure in BTBR mice, and revealed a concomitant rostro-caudal reorganisation of major cortical white matter bundles. Intact inter-hemispheric tracts were found in the anterior commissure, ventro-medial thalamus, and in a strain-specific white matter formation located above the third ventricle. BTBR also exhibited decreased fronto-cortical, occipital and thalamic gray matter volume and widespread reductions in cortical thickness with respect to control B6 mice. Foci of increased gray matter volume and thickness were observed in the medial prefrontal and insular cortex. Mapping of resting-state brain activity using cerebral blood volume weighted fMRI revealed reduced cortico-thalamic function together with foci of increased activity in the hypothalamus and dorsal hippocampus of BTBR mice. Collectively, our results show pronounced functional and structural abnormalities in the brain of BTBR mice with respect to control B6 mice. The large and widespread white and gray matter abnormalities observed do not appear to be representative of the neuroanatomical alterations typically observed in autistic patients. The presence of reduced fronto-cortical metabolism is of potential translational relevance, as this feature recapitulates previously-reported clinical observations

Relationship of white and gray matter abnormalities to clinical and genetic features in myotonic dystrophy type 1

AbstractBackgroundMyotonic dystrophy type 1 (DM1) represents a multisystemic disorder in which diffuse brain white and gray matter alterations related to clinical and genetic features have been described. We aimed to evaluate in the brain of adult patients with DM1 (i) white and gray matter differences, including cortical-subcortical gray matter volume and cortical thickness and (ii) their correlation with clinical disability, global neuropsychological performance and triplet expansion.MethodsWe included 24 adult genetically-confirmed DM1 patients (14 males; age: 38.5±11.8years) and 25 age- and sex-matched healthy controls (14 males; age: 38.5±11.3years) who underwent an identical brain MR protocol including high-resolution 3D T1-weighted, axial T2 FLAIR and DTI sequences. All patients underwent an extensive clinical and neuropsychological evaluation. Voxel-wise analyses of white matter, performed by using Tract Based Spatial Statistics, and of gray matter, with Voxel-based Morphometry and Cortical Thickness, were carried out in order to test for differences between patients with DM1 and healthy controls (p<0.05, corrected). The correlation between MRI measures and clinical-genetic features was also assessed.ResultsPatients with DM1 showed widespread abnormalities of all DTI parameters in the white matter, which were associated with reduced gray matter volume in all brain lobes and thinning in parieto-temporo-occipital cortices, albeit with less extensive cortical alterations when congenital cases were removed from the analyses. White matter alterations correlated with clinical disability, global cognitive performance and triplet expansions.ConclusionIn patients with DM1, the combined smaller overall gray matter volume and white matter alterations seem to be the main morpho-structural substrates of CNS involvement in this condition. The correlation of white matter differences with both clinical and genetic findings lends support to this notion