Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age.

INTRODUCTION: The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life. METHODS: First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images. These manual segmentations are considered the gold standard in assessing the automatic segmentations. MAGeT-Brain, automatic hippocampal segmentation pipeline, requires only a small number of input atlases and reduces the registration and resampling errors by employing an intermediate template library. We assess the segmentation accuracy of MAGeT-Brain in three validation studies, evaluate the hippocampal growth from early-in-life to term-equivalent age, and study the effect of preterm birth on the hippocampal volume. The first experiment thoroughly validates MAGeT-Brain segmentation in three sets of 10-fold Monte Carlo cross-validation (MCCV) analyses with 187 different groups of input atlases and templates. The second experiment segments the neonatal hippocampi on 168 early-in-life and 154 term images and evaluates the hippocampal growth rate of 125 infants from early-in-life to term-equivalent age. The third experiment analyzes the effect of gestational age (GA) at birth on the average hippocampal volume at early-in-life and term-equivalent age using linear regression. RESULTS: The final segmentations demonstrate that MAGeT-Brain consistently provides accurate segmentations in comparison to manually derived gold standards (mean Dice\u27s Kappa \u3e 0.79 and Euclidean distance CONCLUSIONS: MAGeT-Brain is capable of segmenting hippocampi accurately in preterm neonates, even at early-in-life. Hippocampal asymmetry with a larger right side is demonstrated on early-in-life images, suggesting that this phenomenon has its onset in the 3rd trimester of gestation. Hippocampal volume assessed at the time of early-in-life and term-equivalent age is linearly associated with GA at birth, whereby smaller volumes are associated with earlier birth

International Veterinary Epilepsy Task Force recommendations for a veterinary epilepsy-specific MRI protocol

Epilepsy is one of the most common chronic neurological diseases in veterinary practice. Magnetic resonance imaging (MRI) is regarded as an important diagnostic test to reach the diagnosis of idiopathic epilepsy. However, given that the diagnosis requires the exclusion of other differentials for seizures, the parameters for MRI examination should allow the detection of subtle lesions which may not be obvious with existing techniques. In addition, there are several differentials for idiopathic epilepsy in humans, for example some focal cortical dysplasias, which may only apparent with special sequences, imaging planes and/or particular techniques used in performing the MRI scan. As a result, there is a need to standardize MRI examination in veterinary patients with techniques that reliably diagnose subtle lesions, identify post-seizure changes, and which will allow for future identification of underlying causes of seizures not yet apparent in the veterinary literature. There is a need for a standardized veterinary epilepsy-specific MRI protocol which will facilitate more detailed examination of areas susceptible to generating and perpetuating seizures, is cost efficient, simple to perform and can be adapted for both low and high field scanners. Standardisation of imaging will improve clinical communication and uniformity of case definition between research studies. A 6–7 sequence epilepsy-specific MRI protocol for veterinary patients is proposed and further advanced MR and functional imaging is reviewed

Neonatal brain tissue classification with morphological adaptation and unified segmentation

Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation) acquired at 30 weeks’ corrected gestational age (n= 5), coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5) and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation) acquired shortly after birth (n= 12), preterm infants acquired at term-equivalent age (n= 12), and healthy term-born infants (born ≥38 weeks’ gestation) acquired within the first nine days of life (n= 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray matter for coronal images acquired at 30 weeks. This demonstrates that MANTiS’ performance is competitive with existing techniques. For the WUNDeR dataset, mean Dice scores comparing MANTiS with manually edited segmentations demonstrated good agreement, where all scores were above 0.75, except for the hippocampus and amygdala. The results show that MANTiS is able to segment neonatal brain tissues well, even in images that have brain abnormalities common in preterm infants. MANTiS is available for download as an SPM toolbox from http://developmentalimagingmcri.github.io/mantis

A comparison of FreeSurfer-generated data with and without manual intervention

This paper examined whether FreeSurfer - generated data differed between a fully – automated, unedited pipeline and an edited pipeline that included the application of control points to correct errors in white matter segmentation. In a sample of 30 individuals, we compared the summary statistics of surface area, white matter volumes, and cortical thickness derived from edited and unedited datasets for the 34 regions of interest (ROIs) that FreeSurfer (FS) generates. To determine whether applying control points would alter the detection of significant differences between patient and typical groups, effect sizes between edited and unedited conditions in individuals with the genetic disorder, 22q11.2 deletion syndrome (22q11DS) were compared to neurotypical controls. Analyses were conducted with data that were generated from both a 1.5 tesla and a 3 tesla scanner. For 1.5 tesla data, mean area, volume, and thickness measures did not differ significantly between edited and unedited regions, with the exception of rostral anterior cingulate thickness, lateral orbitofrontal white matter, superior parietal white matter, and precentral gyral thickness. Results were similar for surface area and white matter volumes generated from the 3 tesla scanner. For cortical thickness measures however, seven edited ROI measures, primarily in frontal and temporal regions, differed significantly from their unedited counterparts, and three additional ROI measures approached significance. Mean effect sizes for edited ROIs did not differ from most unedited ROIs for either 1.5 or 3 tesla data. Taken together, these results suggest that although the application of control points may increase the validity of intensity normalization and, ultimately, segmentation, it may not affect the final, extracted metrics that FS generates. Potential exceptions to and limitations of these conclusions are discussed

Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies

The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT\u27NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging

Prenatal maternal health and child brain structure: Implications for non-verbal ability and optimizing subcortical segmentation

Brain development starts in utero, and the fetal brain can already be affected by the environment, including chemical exposures and maternal health characteristics. These factors range from exposures to large quantities of teratogens (such as alcohol) to variations in the behaviors and characteristics of healthy individuals (such as age, diet, and subclinical levels of depressive and anxiety symptoms), which can nonetheless have long-lasting adverse effects. In this thesis, we reviewed the literature on the effects of prenatal exposures on human neurodevelopment, as well as cognitive, behavioral, and health outcomes. In Study I we found that prenatal exposures are often reported poorly in infant neuroimaging studies and gave recommendations for reporting in future studies. In Study II, we examined which early life factors predicted cortical structure in 5-year-olds. The results from Study II were utilized to make an informed decision regarding confounders in future studies in the 5-year-old neuroimaging sample of the FinnBrain Birth Cohort study. In Study III, we explored the cortical structural correlates of non-verbal ability in 5-year-olds. The findings were generally in line with prior results from adult and adolescent studies, with the important addition of a positive association between gray matter volume and surface area in the right medial occipital region and non-verbal ability as well as visual abstract reasoning ability. Finally, in Study IV, we compared the results from two common segmentation tools, FSL-FIRST and FreeSurfer, against manual segmentation in the hippocampus and subcortical structures. Overall, the agreement with manual segmentation was good, although results were suboptimal for the hippocampus, amygdala, and nucleus accumbens, and careful visual quality control is still recommended. This thesis summarized different perinatal factors affecting the developing brain, and ensured the high quality of our neuroimaging data. This foundational work, together with the multidisciplinary, longitudinal data collection in the FinnBrain Birth Cohort study, can be used to discover how environmental factors affect brain development.Äidin raskausajan terveys ja lapsen aivojen rakenne: yhteydet ei-kielellisiin taitoihin ja subkortikaalisen segmentaation optimointi Aivojen kehitys alkaa kohdussa ja jatkuu läpi elämän. Jo sikiöaikana aivot ovat alttiina ympäristön vaikutuksille, ml. kemialliset altisteet sekä äidin terveyteen liittyvät tekijät. Nämä altisteet vaihtelevat suurista annoksista teratogeeneille (esim. alkoholille) eroihin terveiden yksilöiden ominaisuuksissa ja toiminnassa (esim. ikä, ruokavalio sekä vähäiset masennus- ja ahdistusoireet ilman mielenterveyshäiriön diagnoosia), joilla voi kuitenkin olla kauaskantoisia seuraamuksia. Tässä väitöskirjassa teemme katsauksen raskaudenaikaisten altisteiden vaikutuksista yksilön kehitykseen sekä siihen liittyviin muutoksiin aivoissa. Tutkimuksessa I toteamme, että raskaudenaikaiset altisteet kuvataan usein puutteellisesti vauvojen aivokuvantamista koskevissa tutkimuksissa ja annamme suosituksia raportoinnista. Tutkimuksessa II tutkimme varhaisten altisteiden yhteyksiä 5-vuotiaiden aivojen rakenteeseen. Tämän tutkimuksen tulokset ohjasivat kontrolloitavien muuttujien valintaa. Tutkimuksessa III tutkimme aivokuoren rakenteen yhteyksiä ei-kielelliseen kognitiiviseen kyvykkyyteen 5-vuotiailla. Tulokset olivat pitkälti linjassa aiempien, vanhemmilla osallistujilla tehtyjen tutkimusten kanssa. Uutena tuloksena löysimme yhteyden oikean takaraivolohkon mediaalisen osan tilavuuden ja pinta-alan olevan yhteydessä ei-kielelliseen kyvykkyyteen sekä erityisesti näönvaraiseen päättelyyn. Tutkimuksessa IV vertailimme kahta yleistä segmentointityökalua (FreeSurfer ja FSL-FIRST) käsin tehtyyn segmentaatioon hippokampuksessa ja aivokuoren alaisissa tumakkeissa. Tulokset vaihtelivat paljon rakenteesta riippuen. Huolellista laadunvarmistusta aivoalueiden koon määrityksen yhteydessä suositellaan vahvasti. Tämä väitöskirja antaa kokonaisvaltaisen ymmärryksen aivoihin vaikuttavista varhaisen elämän altisteista. Yhdessä korkealaatuisen aivokuvantamisdatamme sekä muun FinnBrain-syntymäkohortissa kerättävän aineiston kanssa tätä tietoa voidaan hyödyntää useissa tulevissa aivojen kehitystä selvittävissä tutkimuksissa

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

Neuroimaging for Epilepsy Diagnosis and Management

This chapter will cover the neuroimaging techniques and their application to the diagnostic work up and management of adults and children with new onset or chronic epilepsy. We will focus on the specific indications and requirements of different imaging techniques for the diagnosis and pre-surgical work up of pharmacoresistant focal epilepsies. We will discuss the sensitivity, specificity and prognostic value of imaging features, benign variants and artefacts, and the possible diagnostic significance of non-epileptogenic lesions. This chapter is intended to be relevant for day-to-day practice in average clinical circumstances, with emphasis on MRI and most commonly used functional neuroimaging techniques

Automated brain segmentation methods for clinical quality MRI and CT images

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder associated with brain tissue loss. Accurate estimation of this loss is critical for the diagnosis, prognosis, and tracking the progression of AD. Structural magnetic resonance imaging (sMRI) and X-ray computed tomography (CT) are widely used imaging modalities that help to in vivo map brain tissue distributions. As manual image segmentations are tedious and time-consuming, automated segmentation methods are increasingly applied to head MRI and head CT images to estimate brain tissue volumes. However, existing automated methods can be applied only to images that have high spatial resolution and their accuracy on heterogeneous low-quality clinical images has not been tested. Further, automated brain tissue segmentation methods for CT are not available, although CT is more widely acquired than MRI in the clinical setting. For these reasons, large clinical imaging archives are unusable for research studies. In this work, we identify and develop automated tissue segmentation and brain volumetry methods that can be applied to clinical quality MRI and CT images. In the first project, we surveyed the current MRI methods and validated the accuracy of these methods when applied to clinical quality images. We then developed CTSeg, a tissue segmentation method for CT images, by adopting the MRI technique that exhibited the highest reliability. CTSeg is an atlas-based statistical modeling method that relies on hand-curated features and cannot be applied to images of subjects with different diseases and age groups. Advanced deep learning-based segmentation methods use hierarchical representations and learn complex features in a data-driven manner. In our final project, we develop a fully automated deep learning segmentation method that uses contextual information to segment clinical quality head CT images. The application of this method on an AD dataset revealed larger differences between brain volumes of AD and control subjects. This dissertation demonstrates the potential of applying automated methods to large clinical imaging archives to answer research questions in a variety of studies

Dear reviewers: responses to common reviewer critiques about infant neuroimaging studies

The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT'NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging.R01 MH104324 - NIMH NIH HHS; UL1 TR001863 - NCATS NIH HHS; P50 MH115716 - NIMH NIH HHS; K01 MH108741 - NIMH NIH HHS; TL1 TR001864 - NCATS NIH HHS; R01 MH118285 - NIMH NIH HHS; U01 MH110274 - NIMH NIH HHS; P50 MH100029 - NIMH NIH HHS; ZIA MH002782 - Intramural NIH HHS; R01 EB027147 - NIBIB NIH HHS; R01 MH119251 - NIMH NIH HHS; UL1 TR003015 - NCATS NIH HHS; F31 HD102156 - NICHD NIH HHS; KL2 TR003016 - NCATS NIH HHS; T32 MH018268 - NIMH NIH HHSPublished versio