A COMPUTATIONAL FRAMEWORK FOR NEONATAL BRAIN MRI STRUCTURE SEGMENTATION AND CLASSIFICATION

Deep Learning is increasingly being used in both supervised and unsupervised learning to derive complex patterns from data. However, the successful implementation of deep learning using medical imaging requires careful consideration for the quality and availability of data. Infants diagnosed with CHD are at a higher risk for neurodevelopmental impairment. Many of these deficits may be attenuated by early detection and intervention. However, we currently lack effective diagnostic tools for the reliable detection of these disorders at the neonatal period. We believe that the structural correlates of the cognitive deficits associated with developmental abnormalities can be measured within the first few months of life. Based on this assumption, we hypothesize that we can use an atlas registration based structural segmentation pipeline to sufficiently reduce the search space of neonatal structural brain MRI to viably implement convolutional neural networks for dysplasia classification. Secondly, we hypothesize that convolutional neural networks can successfully identify morphological biomarkers capable of detecting structurally abnormal brain substructures. In this study, we develop a computational framework for the automated classification of dysplastic substructures from neonatal MRI. We validate our implementation on a dataset of neonates born with CHD, as this is a vulnerable population for structural dysmaturation. We chose the cerebellum as the initial test substructure because of its relatively simple structure and known vulnerability to structural dysplasia in infants born with CHD. We then apply the same method to the hippocampus, a more challenging substructure due to its complex morphological properties. We attempt to overcome the limited availability of clinical data in neonatal populations by first extracting each brain substructure of interest and individually registering them into a standard space. This greatly reduces the search space required to learn the subtle abnormalities associated with a given pathology, making it feasible to implement a 3-D CNN as the classification algorithm. We achieved excellent classification accuracy in detecting dysplastic cerebelli, and demonstrate a viable computational framework for search space reduction using limited clinical datasets. All methods developed in this work are designed to be extensible, reproducible, and generalizable diagnostic tools for future neuroimaging problems

Modeling Fetal Brain Development: A semi-automated platform for localization, reconstruction, and segmentation of the fetal brain on MRI

With advances in fetal magnetic resonance imaging (MRI), research in neonatal neuroscience has shifted to identify in utero brain-based biomarkers for outcome prediction in high-risk fetuses, particularly those impacted by growth restriction. Volumetric segmentation of the fetal brain can provide better understanding of the trajectories of brain development and may aid in predicting functional outcomes. The current thesis aimed to develop semi-automatic methods to target deep brain structures in the fetal brain identified on MR images in fetuses with and without growth restriction. In this study, pregnant women (35-39 weeks gestational age [n=9]) with growth appropriate (n=8) and growth restricted fetuses (n=1) were recruited. Fetal MRI was performed on 1.5 Tesla (T) and 3T MRI scanners and 2D stacks of T2-weighted images were acquired. A novel fetal whole brain segmentation algorithm developed for second trimester fetuses was applied to the T2-weighted MR images to reconstruct 2D volumes into 3D images. To segment deep brain structures, an atlas of cortical and subcortical structures was registered to the 3D reconstructed images. Linear and nonlinear registration algorithms, with two types of similarity metrics (mutual information [MI], cross-correlation [CC]), were compared to determine the optimal strategy of segmenting subcortical structures. Dice coefficients were calculated to validate the reliability of automatic methods and to compare the performance between the registration algorithms compared to manual segmentations. Comparing atlas-generated masks against manually segmented masks of the same brain structures, the median Dice-kappa coefficients for linear registration using CC performed optimally. However, post hoc analyses indicated that linear MI and CC performed comparably. Overall, this semi-automatic subcortical segmentation method for third-trimester fetal brain images provides reliable performance. This segmentation pipeline can aid in identifying early predictors of brain dysmaturation to support clinical decision making for antenatal treatment strategies and promote optimal neurodevelopment in fetuses

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Effect of perinatal adversity on structural connectivity of the developing brain

Globally, preterm birth (defined as birth at <37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants