Melanie Ganz

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Colorectal cancer is the third most common type of cancer worldwide. However, this disease can be prevented by detection and removal of precursor adenomatous polyps during optical colonoscopy (OC). During OC, the endoscopist looks for colon polyps. While hyperplastic polyps are benign lesions, adenomatous polyps are likely to become cancerous. Hence, it is(More)
The serotonin (5-HT) system modulates many important brain functions and is critically involved in many neuropsychiatric disorders. We here present a high-resolution multi-dimensional in vivo atlas of four of the human brain's 5-HT receptors (5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4) as well as of the 5-HT transporter (5-HTT). The atlas is created from molecular(More)
BACKGROUND Abdominal aortic calcifications (AAC) predict cardiovascular mortality. A new scoring model for AAC, the Morphological Atherosclerotic Calcification Distribution (MACD) index may contribute with additional information to the commonly used Aortic Calcification Severity (AC24) score, when predicting death from cardiovascular disease (CVD). In this(More)
Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used(More)
The aim of this study is to investigate new methods for describing the progression of atherosclerosis based on novel information of the growth patterns of individual abdominal aortic calcifications (AACs) over time. Lateral X-ray images were used due to their low cost, fast examination time, and wide-spread use, which facilitates a large statistical model(More)
BACKGROUND Aortic calcification is a major risk factor for death from cardiovascular disease. We investigated the relationship between mortality and the composite markers of number, size, morphology and distribution of calcified plaques in the lumbar aorta. METHODS 308 postmenopausal women aged 48-76 were followed for 8.3 ± 0.3 years, with deaths related(More)
Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. Machine-learning based medical image analysis methods provide valuable tools to determine such variations. In particular, image-based multivariate prediction models and “relevant features” they(More)
In this paper, we will re-visit the Relevance Voxel Machine (RVoxM), a recently developed sparse Bayesian framework used for predicting biological markers, e.g., presence of disease, from high-dimensional image data, e.g., brain MRI volumes. The proposed improvement, called IRVoxM, mitigates the shortcomings of the greedy optimization scheme of the original(More)
Random Forest has become one of the most popular tools for feature selection. Its ability to deal with high-dimensional data makes this algorithm especially useful for studies in neuroimaging and bioinformatics. Despite its popularity and wide use, feature selection in Random Forest still lacks a crucial ingredient: false positive rate control. To date(More)
Abdominal aortic calcifications (AACs) correlate strongly with coronary artery calcifications and can be predictors of cardiovascular mortality. We investigated whether size, shape, and distribution of AACs are related to mortality and how such prognostic markers perform compared to the state-of-the-art AC24 marker introduced by Kauppila. Methods. For 308(More)