Longitudinal segmentation of age-related white matter hyperintensities

Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns

Variational inference for medical image segmentation

Variational inference techniques are powerful methods for learning probabilistic models and provide significant advantages over maximum likelihood (ML) or maximum a posteriori (MAP) approaches. Nevertheless they have not yet been fully exploited for image processing applications. In this paper we present a variational Bayes (VB) approach for image segmentation. We aim to show that VB provides a framework for generalising existing segmentation algorithms that rely on an expectation–maximisation formulation, while increasing their robustness and computational stability. We also show how optimal model complexity can be automatically determined in a variational setting, as opposed to ML frameworks which are intrinsically prone to overfitting. Finally, we demonstrate how suitable intensity priors, that can be used in combination with the presented algorithm, can be learned from large imaging data sets by adopting an empirical Bayes approach

Automated hippocampal segmentation in patients with epilepsy: Available free online

Hippocampal sclerosis, a common cause of refractory focal epilepsy, requires hippocampal volumetry for accurate diagnosis and surgical planning. Manual segmentation is time-consuming and subject to interrater/intrarater variability. Automated algorithms perform poorly in patients with temporal lobe epilepsy. We validate and make freely available online a novel automated method

A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality

Quality control (QC) of medical images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually at significant time and operator cost. We aim to automate the process by formulating a probabilistic network that estimates uncertainty through a heteroscedastic noise model, hence providing a proxy measure of task-specific image quality that is learnt directly from the data. By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner. This enables us to predict separate uncertainty quantities for the different types of data degradation. While the uncertainty measures reflect the presence and severity of image artefacts, the network also provides the segmentation predictions given the quality of the data. We show models trained with simulated artefacts provide informative measures of uncertainty on real-world images and we validate our uncertainty predictions on problematic images identified by human-raters

Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI.

Preterm birth is a major public health concern, with the severity and occurrence of adverse outcome increasing with earlier delivery. Being born preterm disrupts a time of rapid brain development: in addition to volumetric growth, the cortex folds, myelination is occurring and there are changes on the cellular level. These neurological events have been imaged non-invasively using diffusion-weighted (DW) MRI. In this population, there has been a focus on examining diffusion in the white matter, but the grey matter is also critically important for neurological health. We acquired multi-shell high-resolution diffusion data on 12 infants born at ≤28weeks of gestational age at two time-points: once when stable after birth, and again at term-equivalent age. We used the Neurite Orientation Dispersion and Density Imaging model (NODDI) (Zhang et al., 2012) to analyse the changes in the cerebral cortex and the thalamus, both grey matter regions. We showed region-dependent changes in NODDI parameters over the preterm period, highlighting underlying changes specific to the microstructure. This work is the first time that NODDI parameters have been evaluated in both the cortical and the thalamic grey matter as a function of age in preterm infants, offering a unique insight into neuro-development in this at-risk population

Mathematical modelling of shallow flows: closure models drawn from grain-scale mechanics of sediment transport and flow hydrodynamics

Canadian Journal of Civil Engineering 36(10) 1605–16Mathematical modelling of river processes is, nowadays, a key element in river engineering and planning. River modelling tools should rest on conceptual models drawn from mechanics of sediment transport, river mechanics, and river hydrodynamics. The objectives of the present work are (i) to describe conceptual models of sediment transport, deduced from grain-scale mechanics of sediment transport and turbulent flow hydrodynamics, and (ii) to present solutions to specific river morphology problems. The conceptual models described are applicable to the morphologic evolution of rivers subjected to the transport of poorly sorted sediment mixtures at low shear stresses and to geomorphic flows featuring intense sediment transport at high shear stresses. In common, these applications share the fact that sediment transport and flow resistance depend, essentially, on grain-scale phenomena. The idealized flow structures are presented and discussed. Numerical solutions for equilibrium and nonequilibrium sediment transport are presented and compared with laboratory and field data

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg

Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

Serological response to a single dose of a SARS-CoV-2 mRNA vaccine

The delays in the production and delivery of COVID-19 vaccines and the growing number of fatal infections across the globe raised the question whether it would be more advantageous to vaccinate a larger group of individuals with one dose instead of a smaller one with two doses. Through a group of vaccinated healthcare workers, we describe the qualitative and quantitative serological response to a single dose of the BNT162b2 vaccine. We found that, before the second dose inoculation, 95.3 % (182/191) already had anti-SARS-CoV-2 IgG and, half of them, antibodies concentrations against RBD (the key target of neutralizing antibodies) that reached maximum values for the used evaluation immunoassay. In order to improve the execution of vaccination programs, further studies are needed to assess whether there are individuals for whom a single dose of mRNA vaccine or a delay in the inoculation of the second dose, produce a sufficient immune response. Additionally, follow-up studies will help in understanding post-vaccination immunity, how long it lasts and how it relates to infection and reinfection.This work was supported by FCT Special Support Research4Covid (Project 186)