Marcel Koek

Erasmus Universiteit Rotterdam, Rotterdam, South Holland, Netherlands

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Publications (11)59.27 Total impact

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    ABSTRACT: We recently introduced an eScience infrastructure for the secure sharing of neuroimaging data and running validated analysis pipelines on a high performance cloud [1]. We have populated this infrastructure with two thousand structural MR images from four Dutch medical centers. As a pilot project, we are segmenting the hippocampus for each of these images, thereby running into a number of practical issues. The most prominent question is whether the pipeline that we use, which has been tuned to perform optimally on data from a single MR scanner, can be directly applied to the four datasets, which differ in resolution, scanner type, and acquisition protocol. The most prominent step of the pipeline [2] is the registration of a set of twenty reference segmentations to the target scan in order to create a probabilistic atlas in target space. This is then combined with an intensity model, and the energy function is minimized via graph cuts. Ideally the pipeline would be able to accept new scans of unknown source, and use a standard set of manual segmentations for registration. We have however observed that the (nonlinear) registration performs worse when the source and target scans have dissimilar tissue intensity scales, which leads to an increased bias and variance of derived results such as the hippocampal volume. An alternative approach is not to use a single set of manual segmentations for all data, but use separate segmentations for each cohort that is added to the platform. This introduces another type of bias when the manual segmentations have been carried out by different investigators using different criteria. We investigate whether the improved statistical power of combining cohorts outweighs the bias and variance introduced by the different scan parameters.
    ICNF2014; 08/2014
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    ABSTRACT: Abstract Objective. There is strong evidence for an association between obesity and esophageal adenocarcinoma (EAC). This study investigated the association between directly measured visceral adipose tissue and the risk of EAC. Methods. In a case-control setting, we measured visceral adipose tissue in patients with EAC and healthy controls. Visceral adipose tissue was determined by abdominal CT. Exclusion criteria were uninterpretable CT scans and severe comorbidity. Controls were healthy volunteers undergoing screening CT colonography. Cross-sectional areas of visceral and subcutaneous adipose tissues were measured in cm(2) at L3/L4. Values of adipose tissue of EAC patients were extrapolated to stage 0 and compared to controls. The association between visceral adipose tissue and EAC was calculated with least-squares regression, adjusted for age, sex and TNM stage. Results. We included 175 EAC patients and 251 controls. While body mass index was similar in EAC patients (26.1 kg/m(2)) and controls (26.2 kg/m(2)), visceral adipose tissue was significantly higher in EAC patients at stage 0 than in controls (276 vs. 231 cm(2); p = 0.015). Regarding subcutaneous adipose tissue, there was no difference. Conclusions. Patients with EAC have significantly higher visceral adipose tissue than healthy controls. Visceral adipose tissue is a risk factor in the development of EAC and seems to be more important than obesity alone.
    Scandinavian Journal of Gastroenterology 01/2014; · 2.33 Impact Factor
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    ABSTRACT: Background / Purpose: Despite current trends towards open science, exclusivity of existing neuroimaging data is considered an asset by many data owners. Our data sharing infrastructure contains strict security features to allow also private sharing, in which only the results of a common analysis pipeline are shared with partners. Main conclusion: The XNAT platform (1) is used to launch pipelines. For data upload we have developed a Java application that pseudonymizes the data before it is transferred over the web.
    Neuroinformatics; 08/2013
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    ABSTRACT: PURPOSE Patterns of brain atrophy and vascular changes, as visualized on brain-MRI, play an important role in determining the underlying cause of dementia syndromes. The visual interpretation of these pathological changes can be very challenging due to intercurrent age-related brain changes. To facilitate the distinction between abnormal and ‘normal age-related’ brain changes, we developed an automated system that provides individual-specific lobar brain volumes and white matter lesion volume taking into account age- and sex-specific reference data from a ‘healthy’ aging population. METHOD AND MATERIALS Automated brain tissue segmentation and atlas-based registration is performed on T1, T2 and FLAIR MR-images, generating lobar brain volumes and white matter lesion volume. For each resulting volume, reference curves are used to generate age- and sex-specific percentile values. The results are then presented in an on-line viewer to interpret the quality of tissue segmentation and the quantitative results. Reference curves are generated from 5000 healthy aging individuals (age-range (years): 46-97, Male/female-ratio (N): 2246/2754). RESULTS An example of segmentation results and percentile plots (colored lines represent percentile-lines) for a 69 year old male patient with suspected dementia is provided in the figure. Visual inspection suggests parietal atrophy, which might be attributed to global cortical atrophy. The automated analysis results show that overall brain volume is appropriate for age (75th to 95th percentile), with a strikingly low parietal lobe volume (37th percentile), confirming the initially suspected parietal lobe atrophy. CONCLUSION A system prototype is currently under evaluation at our radiology department. Future work aims to incorporate automated analysis of microbleeds, shape of individual brain structures and microstructural integrity using diffusion-weighted MRI sequences. CLINICAL RELEVANCE/APPLICATION automated quantification of pathological brain changes with regard to reference data from healthy individuals may facilitate the interpretation of brain MRI in the diagnostic work-up of dementia.
    Radiological Society of North America 2012 Scientific Assembly and Annual Meeting; 11/2012
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    ABSTRACT: The capacity of recognizing the first signs of disease has enormous socio-economic benefits. Population studies have the potential to see disease develop before your eyes, and when including advanced imaging techniques in these studies, literally so. Population imaging studies, especially when complemented with other biomedical and genetic data, provide unique databases that can be exploited with advanced analysis and search techniques for discovering methods for early detection and prediction of disease. This new way of medical research will have considerable impact in the practice of medicine at large. In this presentation we will focus on the development of quantitative imaging biomarkers in neurology using imaging data acquired in a population setting. Currently, effective treatment strategies are lacking in e.g. dementia and stroke. In order to develop such strategies, improved understanding of the early, preclinical stages, of disease, is essential. Quantitative imaging biomarkers for neurologic disease are developed within the context of the Rotterdam Study, a prospective population based study of the causes and determinants of chronic diseases in the elderly that was initiated in 1995. MR brain imaging was performed during this study in random subsets in 1995 and 1999, and since 2005, MR brain imaging is part of the core protocol of the Rotterdam Study. The large scale acquisition of MR brain imaging within the Rotterdam Study allows us to study whether morphologic brain pathology is already present years before clinical onset of neurologic disease, and whether MRI based measurements may be used for prognosis. More information on the Rotterdam Scan Study can be found in [1]. Within the context of the Rotterdam Scan Study, a standardized and validated image analysis workflow is being developed to enable the objective, accurate, and reproducible extraction of relevant parameters describing brain anatomy, possible brain pathologies, and brain connectivity from multispectral MRI data. Image processing in the Rotterdam Scan Study has four main goals: First, owing to the sheer size and complexity of the imaging database being generated, automation of the tedious task of manual analysis is required. Second, qualitative image assessment should be replaced by objective quantitative analyses as much as possible. Third, we aim to limit or avoid altogether inter- and intraobserver variability. Fourth, image processing allows the extraction of relevant image-derived parameters that would not be feasible manually or cannot be assessed visually. This presentation will provide an overview of different quantitative imaging biomarkers that have been developed, or are currently developed as part of the Rotterdam Scan studies. These include brain tissue quantification (grey matter, white matter, also quantified per lobe), quantification of cerebrospinal fluid, volume and shape of neurostructures such as the hippocampus, ventricles and cerebellum, brain connectivity based on diffusion tensor MRI, and vascular brain pathologies such as white matter lesions and microbleeds.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2012;
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    ABSTRACT: In this study we propose a novel method for semi---automated 3D quantification of subcutaneous and visceral adipose tissue from CTA data. The method differentiates between subcutaneous and visceral adipose tissue by using gradient based deformable models using simplex meshes. The performance of the method is evaluated against a reference standard containing 27 manually annotated CTA scans made by expert observers. The quality of the reference standard is assessed by intra- and interobserver variability. The performance of the semi---automated method is evaluated against the reference standard by Pearson linear correlation and Bland and Altman analysis.
    Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications; 09/2011
  • The Lancet 07/2011; 378(9787):227; author reply 228. · 39.21 Impact Factor
  • Gastroenterology 01/2011; 140(5). · 12.82 Impact Factor
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    ABSTRACT: The prevalence of childhood obesity is increasing rapidly. Visceral fat plays an important role in the pathogenesis of metabolic and cardiovascular diseases. Currently, computed tomography (CT) is broadly seen as the most accurate method of determining the amount of visceral fat. The main objective was to examine whether measures of abdominal visceral fat can be determined by ultrasound in children and whether CT can be replaced by ultrasound for this purpose. To assess whether preperitoneal fat thickness and area are good approximations of visceral fat at the umbilical level, we first retrospectively examined 47 CT scans of nonobese children (body mass index <30kg/m(2); median age 7.9 y [95% range 1.2 to 16.2]). Correlation coefficients between visceral and preperitoneal fat thickness and area were 0.58 (p<0.001) and 0.76 (p<0.001), respectively. Then, to assess how preperitoneal and subcutaneous fat thicknesses and areas measured by ultrasound compare with these parameters in CT, we examined 34 nonobese children (median age 9.5 [95% range 0.3 to 17.0]) by ultrasound and CT. Ultrasound measurements of preperitoneal and subcutaneous fat were correlated with CT measurements, with correlation coefficients ranging from 0.75-0.97 (all p<0.001). Systematic differences of up to 24.0cm(2) for preperitoneal fat area (95% confidence interval -29.9 to 77.9cm(2)) were observed when analyzing the results described by the Bland-Altman method. Our findings suggest that preperitoneal fat can be used as an approximation for visceral fat in children and that measuring abdominal fat with ultrasound in children is a valid method for epidemiological and clinical studies. However, the exact agreement between the ultrasound and CT scan was limited, which indicates that ultrasound should be used carefully for obtaining exact fat distribution measurements in individual children
    Ultrasound in Medicine & Biology 12/2009; · 2.46 Impact Factor
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    ABSTRACT: The prevalence of childhood obesity is increasing rapidly. Visceral fat plays an important role in the pathogenesis of metabolic and cardiovascular diseases. Currently, computed tomography (CT) is broadly seen as the most accurate method of determining the amount of visceral fat. The main objective was to examine whether measures of abdominal visceral fat can be determined by ultrasound in children and whether CT can be replaced by ultrasound for this purpose. To assess whether preperitoneal fat thickness and area are good approximations of visceral fat at the umbilical level, we first retrospectively examined 47 CT scans of nonobese children (body mass index <30kg/m(2); median age 7.9 y [95% range 1.2 to 16.2]). Correlation coefficients between visceral and preperitoneal fat thickness and area were 0.58 (p<0.001) and 0.76 (p<0.001), respectively. Then, to assess how preperitoneal and subcutaneous fat thicknesses and areas measured by ultrasound compare with these parameters in CT, we examined 34 nonobese children (median age 9.5 [95% range 0.3 to 17.0]) by ultrasound and CT. Ultrasound measurements of preperitoneal and subcutaneous fat were correlated with CT measurements, with correlation coefficients ranging from 0.75-0.97 (all p<0.001). Systematic differences of up to 24.0cm(2) for preperitoneal fat area (95% confidence interval -29.9 to 77.9cm(2)) were observed when analyzing the results described by the Bland-Altman method. Our findings suggest that preperitoneal fat can be used as an approximation for visceral fat in children and that measuring abdominal fat with ultrasound in children is a valid method for epidemiological and clinical studies. However, the exact agreement between the ultrasound and CT scan was limited, which indicates that ultrasound should be used carefully for obtaining exact fat distribution measurements in individual children.
    Ultrasound in medicine & biology 09/2009; 35(12):1938-46. · 2.46 Impact Factor
  • ECR Conference Proceedings 2011;