Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol

Deep and cortical gray matter volumetric of extremely low gestational age and full term newborn children at 9 to 11 years of age

PURPOSE: Extremely low gestation age newborns (ELGANs) are at high risk for developmental brain abnormalities. This study is to determine deep and superficial gray matter volumetric abnormalities of ELGAN children and full term children at 9 to 11 years of age. METHODS: High-resolution magnetic resonance imaging (MRI) scans were obtained from 160 ELGAN children (70 males and 90 females) and 30 full term children (15 males and 15 females) using a dual-echo turbo spin-echo (DE-TSE) pulse sequence at 3.0T (or 1.5T at only one site). The DICOM MR images were processed with quantitative MRI algorithms programmed in Mathcad. The brain deep gray matter (dGM) was manually segmented; dGM and cortical gray matter (cGM) volumes were quantified using semi-automated clustering segmentation algorithms. RESULTS: ELGAN children had smaller deep gray matter volume (41.86 ± 7.42 ml) than full term children (49.24 ± 10.91 ml). Deep gray matter volumes of ELGAN children showed similar distribution range (SD = 7.42 ml) with the full term children (SD = 10.91 ml). About 83% of the ELGAN children had smaller deep gray matter volumes compared to the average volume of full term children at the same ages. Male children had smaller deep gray matter volumes in ELGAN (42.77 ± 7.09 ml) than in full term (51.74 ± 9.76 ml), but female children had similar deep gray matter volumes in ELGAN (41.14 ± 7.62 ml) with full term (44.27 ± 7.56 ml). Additionally, smaller deep gray matter volumes were observed more often in males (90%) than in females (65%). Cortical gray matter volumes of ELGAN children distributed from 345.60 to 1177.50ml. Moreover, female ELGAN children had smaller cortical gray matter volumes (828.14 ± 147.61 ml) than males (883.13 ± 151.34 ml). Correlation analysis revealed a positive correlation between cerebral deep gray matter volumes and total gray matter volumes (total: r = 0.57, p<0.0001; male: r = 0.542, p < 0.0001; female: r = 0.587, p < 0.0001). CONCLUSION: Male ELGAN children had smaller brain deep gray matter volumes than full term children at ages of 9 to 11 years, but not females. Cortical gray matter volumes of female ELGAN were smaller than male ELGAN. Smaller deep gray matter volumes were associated with smaller total gray matter volumes in ELGAN children

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two years of life, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at 7 time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development

The Brains of Babies: A Surface Based Approach To Study Cortical Development in Term and Preterm Human Infants

Half a million infants are born before term gestation each year in the United States. Although advances in newborn medicine have increased survival rates of very preterm infants to almost 90%, surviving preterm infants are at increased risk for developing lasting neurologic impairments. In order to develop a plausible neuroprotective strategy it is imperative that we improve our understanding of normal cortical development and develop tools to evaluate injury. Using a surface based approach we have characterized normal cortical development in healthy term infants and analyzed abnormalities associated with preterm birth. Accurate cortical surface reconstructions for each hemisphere of 12 healthy term gestation infants and 12 low-risk preterm infants at term equivalent postmenstrual age were generated from structural magnetic resonance imaging data using a novel segmentation algorithm. Data from the 12 term infants were used to establish the first population average surface based atlas of human cerebral cortex at term gestation. Comparing this atlas to a previously established atlas of adult cortex revealed that cortical structure in term infants is similar to the adult in many respects, including the pattern of individual variability and the presence of statistically significant structural asymmetries in lateral temporal cortex, suggesting that that several features of cortical shape are minimally reliant on the postnatal environment. Surprisingly, the pattern of postnatal expansion in surface area is strikingly non-uniform; regions of lateral temporal, parietal, and frontal cortex expand nearly twice as much as other regions in insular and medial occipital cortex. Differential expansion may point to differential sensitivity of cortical circuits to normal or aberrant childhood experiences. The pattern of human postnatal expansion parallels the pattern of evolutionary cortical expansion revealed by comparison between the human and the macaque monkey. Finally, in comparing term and preterm infants, region-specific alterations in cortical folding in the preterm population were found. The most striking shape differences were present in the orbitofrontal and inferior occipital regions with reductions in folding in the insular, lateral temporal, lateral parietal, and lateral frontal cortex. Overall these findings improve our understanding of normal cortical development and help elucidate the potential pathways for cortical injury in preterm infants

Application of Advanced MRI to Fetal Medicine and Surgery

Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution. This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development. I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death

Learning from Complex Neuroimaging Datasets

Advancements in Magnetic Resonance Imaging (MRI) allowed for the early diagnosis of neurodevelopmental disorders and neurodegenerative diseases. Neuroanatomical abnormalities in the cerebral cortex are often investigated by examining group-level differences of brain morphometric measures extracted from highly-sampled cortical surfaces. However, group-level differences do not allow for individual-level outcome prediction critical for the application to clinical practice. Despite the success of MRI-based deep learning frameworks, critical issues have been identified: (1) extracting accurate and reliable local features from the cortical surface, (2) determining a parsimonious subset of cortical features for correct disease diagnosis, (3) learning directly from a non-Euclidean high-dimensional feature space, (4) improving the robustness of multi-task multi-modal models, and (5) identifying anomalies in imbalanced and heterogeneous settings. This dissertation describes novel methodological contributions to tackle the challenges above. First, I introduce a Laplacian-based method for quantifying local Extra-Axial Cerebrospinal Fluid (EA-CSF) from structural MRI. Next, I describe a deep learning approach for combining local EA-CSF with other morphometric cortical measures for early disease detection. Then, I propose a data-driven approach for extending convolutional learning to non-Euclidean manifolds such as cortical surfaces. I also present a unified framework for robust multi-task learning from imaging and non-imaging information. Finally, I propose a semi-supervised generative approach for the detection of samples from untrained classes in imbalanced and heterogeneous developmental datasets. The proposed methodological contributions are evaluated by applying them to the early detection of Autism Spectrum Disorder (ASD) in the first year of the infant’s life. Also, the aging human brain is examined in the context of studying different stages of Alzheimer’s Disease (AD).Doctor of Philosoph

Preprocessing methods for morphometric brain analysis and quality assurance of structural magnetic resonance images

Gegenstand der Dissertation ist die Neuentwicklung und Validierung von Verfahren zur Aufbereitung von anatomischen Daten, die mittels Magnetresonanztomographie gewonnen wurden. Ziel ist dabei die Erfassung von morphometrischen Kennwerten zur Beschreibung der Struktur und Form des Gehirns, wie beispielsweise Volumen, Fläche, Dicke oder Faltung der Großhirnrinde. Die Kennwerte erlauben sowohl die Erforschung individueller gesunder und pathologischer Entwicklung als auch der evolutionären Anpassung des Gehirns. Die zur Datenanalyse notwendige Vorverarbeitung beinhaltet dabei die Angleichung von Bildeigenschaften und individueller Anatomie. Die fortlaufende Weiterentwicklung der Scanner- und Rechentechnik ermöglicht eine zunehmend genauere Bildgebung, erfordert aber die kontinuierliche Anpassung existierender Verfahren. Die Schwerpunkte dieser Dissertation lagen in der Entwicklung neuer Verfahren zur (i) Klassifikation der Hirngewebe (Segmentierung), (ii) räumlichen Abbildung des individuellen Gehirns auf ein Durchschnittsgehirn (Registrierung), (iii) Bestimmung der Dicke der Großhirnrinde und Rekonstruktion einer repräsentativen Oberfläche und (iv) Qualitätssicherung der Eingangsdaten. Die Segmentierung gleicht die Bildeigenschaften unterschiedlicher Protokolle an, während die Registrierung anatomische Merkmale normalisiert und so den Vergleich verschiedener Gehirne ermöglicht. Die Rekonstruktion von Oberflächen erlaubt wiederum die Gewinnung einer Vielzahl weiterer morphometrischer Maße zur spezifischen Charakterisierung des Gehirns und seiner Entwicklung. Anhand von simulierten und realen Daten wird die Validität der neuen Methoden belegt und mit anderen Ansätzen verglichen. Die Verfahren sind Bestandteil der Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), deren Schwerpunkt die Vorverarbeitung von strukturellen Daten ist und die Teil des Statistical Parametric Mapping (SPM) Softwarepaketes in MATLAB ist.This Ph.D. thesis focuses on the development, optimization and validation of preprocessing methods of structural magnetic resonance images of the brain. The preprocessing describes the creation of morphometric data that support a statistical analysis of brain anatomy. Image interferences have to be removed to allow a tissue classification (segmentation). In order to compare different subjects a spatial normalization to an average-shaped brain (template) is required, where atlas maps allow identification of specific brain structures and regions of interest. Beside the analysis in a voxel-grid, the cortex can be represented by surfaces that allow further measures such as the cortical thickness or folding. The derived brain features (such as volume, area, and thickness) permit the individual study of normal and pathological development during the lifespan but also of the evolutionary adaption of the brain. The ongoing progress of imaging and computing technology demands continous enhancement of preprocessing tools but also facilitates the exploration of novel approaches and models. The basis of this thesis is the development of a method that uses a tissue segmentation to estimate the cortical thickness and the central surface in one integrated step. Further essential improvements of surface reconstruction algorithms were achieved by specific refinement of processing steps such as (i) the classification of brain tissue (segmentation), (ii) the spatial mapping of the individual brain to an average brain (registration), (iii) determining the thickness of the cerebral cortex and reconstructing a representative surface and (iv) the quality assurance of input data. The validity of the new methods is proven and compared with other approaches by simulated and real data. The procedures are part of the Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), which focuses on the preprocessing of structural data and is part of the Statistical Parametric Mapping (SPM) software package in MATLAB