Wiro J. Niessen

Bio: Wiro J. Niessen is an academic researcher from Erasmus University Rotterdam. The author has contributed to research in topics: Population & Image registration. The author has an hindex of 85, co-authored 571 publications receiving 30461 citations. Previous affiliations of Wiro J. Niessen include Rotterdam University of Applied Sciences & University of Bonn.

Muliscale Vessel Enhancement Filtering

Abstract: The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter. A vesselness measure is obtained on the basis of all eigenvalues of the Hessian. This measure is tested on two dimensional DSA and three dimensional aortoiliac and cerebral MRA data. Its clinical utility is shown by the simultaneous noise and background suppression and vessel enhancement in maximum intensity projections and volumetric displays.

Incidental findings on brain MRI in the general population.

Abstract: Background Magnetic resonance imaging (MRI) of the brain is increasingly used both in research and in clinical medicine, and scanner hardware and MRI sequences are continually being improved. These advances are likely to result in the detection of unexpected, asymptomatic brain abnormalities, such as brain tumors, aneurysms, and subclinical vascular pathologic changes. We conducted a study to determine the prevalence of such incidental brain findings in the general population. Methods The subjects were 2000 persons (mean age, 63.3 years; range, 45.7 to 96.7) from the population-based Rotterdam Study in whom high-resolution, structural brain MRI (1.5 T) was performed according to a standardized protocol. Two trained reviewers recorded all brain abnormalities, including asymptomatic brain infarcts. The volume of white-matter lesions was quantified in milliliters with the use of automated postprocessing techniques. Two experienced neuroradiologists reviewed all incidental findings. All diagnoses were based o...

Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group.

Abstract: The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.

Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group

Abstract: The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen’s d=−0.14, % difference=−1.24). This effect was driven by patients with recurrent MDD (Cohen’s d=−0.17, % difference=−1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen’s d=−0.20, % difference=−1.85) and a trend toward smaller amygdala (Cohen’s d=−0.11, % difference=−1.23) and larger lateral ventricles (Cohen’s d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

Prevalence and risk factors of cerebral microbleeds The Rotterdam Scan Study

Abstract: Background: Cerebral microbleeds are focal deposits of hemosiderin that can be visualized with MRI. Little is known on their prevalence in the general population and on their etiology. It has been suggested that, in analogy to spontaneous intracranial hemorrhage, the etiology of microbleeds differs according to their location in the brain, with lobar microbleeds being caused by cerebral amyloid angiopathy and deep or infratentorial microbleeds resulting from hypertension and atherosclerosis. We investigated the prevalence of and risk factors for microbleeds in the general population aged 60 years and older. Methods: This study is based on 1,062 persons (mean age 69.6 years) from the population-based Rotterdam Scan Study. MRI was performed at 1.5 T and included a sequence optimized to increase the conspicuity of microbleeds. We assessed the relation of APOE genotype, cardiovascular risk factors, and markers of small vessel disease to the presence and location of microbleeds with multiple logistic regression. Results: Overall prevalence of cerebral microbleeds was high and increased with age from 17.8% in persons aged 60-69 years to 38.3% in those over 80 years. APOE e4 carriers had significantly more often strictly lobar microbleeds than noncarriers. In contrast, cardiovascular risk factors and presence of lacunar infarcts and white matter lesions were associated with microbleeds in a deep or infratentorial location but not in a lobar location. Conclusion: The prevalence of cerebral microbleeds is high. Our data support the hypothesis that strictly lobar microbleeds are related to cerebral amyloid angiopathy, whereas microbleeds in a deep or infratentorial location result from hypertensive or atherosclerotic microangiopathy.

Using Multivariate Statistics

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Image processing with ImageJ

Abstract: Wayne Rasband of NIH has created ImageJ, an open source Java-written program that is now at version 1.31 and is used for many imaging applications, including those that that span the gamut from skin analysis to neuroscience. ImageJ is in the public domain and runs on any operating system (OS). ImageJ is easy to use and can do many imaging manipulations. A very large and knowledgeable group makes up the user community for ImageJ. Topics covered are imaging abilities; cross platform; image formats support as of June 2004; extensions, including macros and plug-ins; and imaging library. NIH reports tens of thousands of downloads at a rate of about 24,000 per month currently. ImageJ can read most of the widely used and significant formats used in biomedical images. Manipulations supported are read/write of image files and operations on separate pixels, image regions, entire images, and volumes (stacks in ImageJ). Basic operations supported include convolution, edge detection, Fourier transform, histogram and particle analyses, editing and color manipulation, and more advanced operations, as well as visualization. For assistance in using ImageJ, users e-mail each other, and the user base is highly knowledgeable and will answer requests on the mailing list. A thorough manual with many examples and illustrations has been written by Tony Collins of the Wright Cell Imaging Facility at Toronto Western Research Institute and is available, along with other listed resources, via the Web.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.