Esther E Bron

Esther E Bron
Erasmus MC | Erasmus MC · Biomedical Imaging Group

PhD

About

104
Publications
14,167
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1,671
Citations
Additional affiliations
September 2011 - September 2015
Erasmus MC
Position
  • PhD Student

Publications

Publications (104)
Article
Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnosti...
Article
Full-text available
Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features can be quantified from the SVM weights as a significance map (p-map). Although these p-maps previously showed clusters of relevant voxels in dementia-related brain regions, they have not yet been use...
Article
Full-text available
Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image...
Article
Full-text available
Objectives: To evaluate the effect of automated registration in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) of the knee on the occurrence of movement artefacts on the T1 map and the reproducibility of region-of-interest (ROI)-based measurements. Methods: Eleven patients with early-stage knee osteoarthritis and ten healthy controls und...
Preprint
Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and the types of output they provi...
Article
Background Children with trigonocephaly are at risk for neurodevelopmental disorders. The aim of this study is to investigate white matter properties of the frontal lobes in young, unoperated patients with metopic synostosis as compared to healthy controls using Diffusion Tension Imaging (DTI). Methods Preoperative DTI datasets of 46 patients with...
Article
Full-text available
Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in...
Article
Biological processes underlying cerebral small vessel disease (cSVD) are largely unknown. We hypothesized that identification of clusters of inter-related bood-based biomarkers that are associated with the burden of cSVD provides leads on underlying biological processes. In 494 participants (mean age 67.6 ± 8.7 years; 36% female; 75% cardiovascular...
Preprint
Full-text available
Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in...
Article
Several fluid biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), gliosis (glial fibrillary acidic protein (GFAP)) and complement activation (C3b,...
Article
Several CSF and blood biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), astrogliosis (glial fibrillary acidic protein (GFAP)), and complement ac...
Chapter
Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice. We consider the problem of learning representations independent to multiple biases. In literature, this is mostly solved by purging the bias information from learned representations. We however expect this strategy to harm the diversit...
Article
Background: Emerging evidence shows sex differences in manifestations of vascular brain injury in memory clinic patients. We hypothesize that this is explained by sex differences in cardiovascular function. Objective: To assess the relation between sex and manifestations of vascular brain injury in patients with cognitive complaints, in interact...
Preprint
Full-text available
Radiomics uses quantitative medical imaging features to predict clinical outcomes. While many radiomics methods have been described in the literature, these are generally designed for a single application. The aim of this study is to generalize radiomics across applications by proposing a framework to automatically construct and optimize the radiom...
Article
Full-text available
For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potenti...
Preprint
Full-text available
Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice. We consider the problem of learning representations independent to multiple biases. In literature, this is mostly solved by purging the bias information from learned representations. We however expect this strategy to harm the diversit...
Article
Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled s...
Article
Full-text available
This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) ap...
Article
Full-text available
Cerebral cortical microinfarcts (CMI) are small ischemic lesions that are associated with cognitive impairment and probably have multiple etiologies. Cerebral hypoperfusion has been proposed as a causal factor. We studied CMI in patients with internal carotid artery (ICA) occlusion, as a model for cerebral hemodynamic compromise. We included 95 pat...
Article
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and optimiz...
Article
Full-text available
Objective Progranulin-related frontotemporal dementia (FTD- GRN ) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for F...
Preprint
Full-text available
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and optimiz...
Article
Background The prevalence of end-stage renal disease (ESRD) is increasing worldwide, with the majority of new ESRD cases diagnosed in patients aged >60 years. These older patients are at increased risk for impaired cognitive functioning, potentially through cerebral small vessel disease (SVD). Novel markers of vascular integrity may be of clinical...
Preprint
Full-text available
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) a...
Article
Progranulin related frontotemporal dementia (FTD‐GRN) is a fast progressive disorder, in which pathophysiological changes precede overt clinical symptoms in only a short time period. Modelling the cascade of multimodal biomarker changes aids in understanding the etiology of this disease, enables monitoring of individual mutation carriers, and would...
Article
Cerebral cortical microinfarcts (CMI) are small ischemic lesions that are associated with cognitive impairment and are often detected in patients with dementia, also in patients with a clinical diagnosis of Alzheimer’s disease (AD). CMI probably have multiple etiologies. Cerebral hypoperfusion has also been proposed as a causal factor. We studied C...
Article
Cognitive impairment in heart failure (HF) interferes with the capacity to self‐care and is associated with adverse health outcomes. This underlines the importance of detecting the extent and nature of cognitive impairment in HF. We report on the presence and mutual association of neuroimaging markers and cognitive impairment in patients with HF. W...
Article
Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials, at the right time. However, an unbiased comparison of state‐of‐the‐art algorithms for predicting disease onset and progression is currently lacking. We present the findings of The Alzheimer's Disease Predicti...
Article
Accurate diagnosis and prediction of Alzheimer's disease (AD) is important for care planning and clinical trials. Machine learning based on MRI showed promising results for these tasks. While in many medical imaging applications deep neural networks have outperformed conventional approaches, this has not been shown for AD classification. Therefore,...
Article
Full-text available
Alzheimer’s disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-drive...
Article
Full-text available
Background As disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach. Methods We analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-les...
Conference Paper
Full-text available
Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based regis...
Preprint
Full-text available
Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based regis...
Article
Background Cognitive impairment in heart failure (HF) interferes with the capacity to self-care and is associated with adverse health outcomes. This underlines the importance of detecting the extent and nature of cognitive impairment in HF. We report on the presence and mutual association of neuroimaging markers and cognitive impairment in patients...
Article
Full-text available
Background: Transcatheter aortic valve implantation (TAVI) is a minimally invasive, life-saving treatment for patients with severe aortic valve stenosis that improves quality of life. We examined cardiac output and cerebral blood flow in patients undergoing TAVI to test the hypothesis that improved cardiac output after TAVI is associated with an i...
Preprint
Full-text available
Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-drive...
Preprint
Full-text available
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinic...
Article
Full-text available
Introduction: We examined the role of hemodynamic dysfunction in cognition by relating cerebral blood flow (CBF), measured with arterial spin labeling (ASL), to cognitive functioning, in patients with heart failure (HF), carotid occlusive disease (COD), and patients with cognitive complaints and vascular brain injury on magnetic resonance imaging...
Article
Full-text available
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinic...
Article
Full-text available
Background Patients with heart failure (HF) are at risk for vascular brain injury. Cerebral cortical microinfarcts (CMIs) are a novel MRI marker of vascular brain injury. This study aims to determine the occurrence of CMIs in patient with HF and their clinical correlates, including haemodynamic status. Methods From the Heart-Brain Study, a multice...
Preprint
Full-text available
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of...
Article
Full-text available
Objective: To investigate the relationship between Alzheimer's disease biomarkers and neuropsychiatric symptoms. Methods: Data from two large cohort studies, the Dutch Parelsnoer Institute - Neurodegenerative Diseases and the Alzheimer's Disease Neuroimaging Initiative was used, including subjects with subjective cognitive decline (N = 650), mil...
Preprint
Full-text available
The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of thre...
Article
Full-text available
Chronic silent brain infarctions, detected as new white matter hyperintensities on magnetic resonance imaging (MRI) following transcatheter aortic valve implantation (TAVI), are associated with long-term cognitive deterioration. This is the first study to investigate to which extent the calcification volume of the native aortic valve (AV) measured...
Article
Full-text available
Phenocopy frontotemporal dementia (phFTD) shares core characteristics with behavioral variant frontotemporal dementia (bvFTD), yet without associated cognitive deficits and brain abnormalities on conventional magnetic resonance imaging (MRI), and without progression. Using advanced MRI techniques, we previously observed subtle structural and functi...
Article
Background and Purpose— Nonfocal transient neurological attacks (TNAs), such as unsteadiness, bilateral weakness, or confusion, are associated with an increased risk of stroke and dementia. Cerebral ischemia plays a role in their pathogenesis, but the precise mechanisms are unknown. We hypothesized that cerebral small vessel disease is involved in...
Chapter
To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to p...
Chapter
Full-text available
The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer’s disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) stud...
Conference Paper
Full-text available
The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer’s disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) stud...
Article
Background Recent studies suggest that cardiovascular disease and dementia are closely related, which led to the concept of a “heart-brain axis”. Dysfunction in any component of the heart-brain axis could be a risk factor for the development of brain damage and consequently to the development of cognitive impairment. In the Heart-Brain study, we fo...
Book
Full-text available
The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer’s disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) stud...
Preprint
Full-text available
Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract...
Preprint
Full-text available
To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to p...
Article
Full-text available
Introduction: Nonfocal transient neurological attacks (TNAs) are associated with an increased risk of cardiac events, stroke and dementia. Their etiology is still unknown. Global cerebral hypoperfusion has been suggested to play a role in their etiology, but this has not been investigated. We assessed whether lower total brain perfusion is associa...
Article
Full-text available
Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DE...
Article
Full-text available
Background Semantic dementia (SD) is a neurodegenerative disorder characterised by progressive language problems falling within the clinicopathological spectrum of frontotemporal lobar degeneration (FTLD). The development of disease-modifying agents may be facilitated by the relative clinical and pathological homogeneity of SD, but we need robust m...
Chapter
Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentia...
Article
Full-text available
Objectives This study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer’s disease (AD) and behavioural variant frontote...