Alexander Oliver Mader

Alexander Oliver Mader
Fachhochschule Kiel · Institute of Applied Computer Science

Master of Science, M.Sc.

About

9
Publications
613
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35
Citations

Publications

Publications (9)
Chapter
Automated evaluation of vertebral fracture status on computed tomography (CT) scans acquired for various purposes (opportunistic CT) may substantially enhance vertebral fracture detection rate. Convolutional neural networks (CNNs) have shown promising performance in numerous tasks but their black box nature may hinder acceptance by physicians. We a...
Chapter
Low back pain is a leading cause of disability that has been associated with intervertebral disc (IVD) degeneration by various clinical studies. With MRT being the imaging technique of choice for IVDs due to its excellent soft tissue contrast, we propose a fully automatic approach for localizing and locally segmenting spatially correlated objects—t...
Chapter
The fully automatic localization of key points in medical images is an important and active area in applied machine learning, with very large sets of key points still being an open problem. To this end, we extend two general state-of-the-art localization approaches to operate on large amounts of key points and evaluate both approaches on a CT spine...
Article
The automatic detection and accurate localization of landmarks is a crucial task in medical imaging. It is necessary for tasks like diagnosis, surgical planning, and post-operative assessment. A common approach to localize multiple landmarks is to combine multiple independent localizers for individual landmarks with a spatial regularizer, e.g., a c...
Conference Paper
Full-text available
MAP inference over discrete Markov networks with large label sets is often applied, e.g., in localizing multiple key points in the image domain. Often, approximate or domain specific methods are used to make the problem feasible. An alternative method is to preselect a limited (much smaller) set of suitable labels, which bears the risk to exclude t...
Chapter
Localization and labeling of posterior ribs in radiographs is an important task and a prerequisite for, e.g., quality assessment, image registration, and automated diagnosis. In this paper, we propose an automatic, general approach for localizing spatially correlated landmarks using a fully convolutional network (FCN) regularized by a conditional r...
Conference Paper
The detection and localization of single or multiple landmarks is a crucial task in medical imaging. It is often required as initialization for other tasks like segmentation or registration. A common approach to localize multiple landmarks is to exploit their spatial correlations, e.g., by using a conditional random field (CRF) to incorporate geome...
Chapter
Accurate localization of sets of anatomical landmarks is a challenging task, yet often required in automatic analysis of medical images. Several groups – e.g., Donner et al. – have shown that it is beneficial to incorporate geometrical relations of landmarks into detection procedures for complex anatomical structures. In this paper, we present a tw...

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