
Andreas NürnbergerOtto-von-Guericke University Magdeburg | OvGU · Faculty of Computer Science
Andreas Nürnberger
Prof. Dr.
About
451
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Introduction
Andreas Nürnberger currently works at the Faculty of Computer Science, Otto-von-Guericke-Universität Magdeburg. Andreas does research in Computer Science, Information Retrieval, HCI, Artificial Intelligence and Artificial Neural Network.
Additional affiliations
October 2007 - present
May 2003 - September 2007
May 2001 - April 2003
Publications
Publications (451)
Objectives
The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). H...
Background
Transbronchial needle biopsy is crucial for diagnosing lung cancer, yet its efficacy depends on accurately localizing the target lesion and biopsy needle. Digital tomosynthesis (DTS) is considered a promising imaging modality for guiding bronchoscopy procedures due to its low radiation dose and small footprint relative to cone‐beam compu...
The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acq...
High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less spatial coverage, lower signal-to-noise ratio (SNR), and longer scan time due to physical, physiologic...
Designing user-adaptive search systems necessitates modeling the user's knowledge state during information seeking. Gaze data offers insights into cognitive processes during task-based reading. Despite its potential, cognitive perspectives have been insufficiently explored in the representation of the user's knowledge state when designing search sy...
Identification of vessel structures of different sizes in biomedical images is crucial in the diagnosis of many neurodegenerative diseases. However, the sparsity of good-quality annotations of such images makes the task of vessel segmentation challenging. Deep learning offers an efficient way to segment vessels of different sizes by learning their...
Hyperthermia (HT) in combination with radio-and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures of 39 to 43 °C for 60 minutes. Temperature monitoring can be performed noninvasively using dynamic magnetic resonance imaging (MRI). However, the slow...
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing of infected patients. Medical imaging, such as X-ray and computed tomography (CT), combined with the potential of artificial intelligence (A...
Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisitions with high temporal resolution suffer from limited spatial resolution -...
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadin...
We present MedShapeNet, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D surgical instrument models. Prior to the deep learning era, the broad application of statistical shape models (SSMs) in medical image analysis is evidence that shapes have been commonly used to describe medical data. Nowadays, however, state-of-the...
Computed tomography (CT) and magnetic resonance imaging (MRI) are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse samplin...
We examine the use of prototypes and criticisms for explaining clusterings in digital public participation processes of the e-participation domain. These processes enable people to participate in various life areas such as landscape planning by submitting contributions that express their opinions or ideas. Clustering groups similar contributions to...
Neural approaches, which are currently state-of-the-art in many areas, have contributed significantly to the exciting advancements in machine translation. However, Neural Machine Translation (NMT) requires a substantial quantity and good quality parallel training data to train the best model. A large amount of training data, in turn, increases the...
Iterative undersampled MRI reconstructions, such as compressed sensing, can reconstruct undersampled MRIs - but due to their slow execution speed, they are not suitable for real-time applications. Several deep learning approaches have been proposed, mostly working in image space. Some of the approaches, which work on the k-space or in a mix of spac...
Disagreements among the experts while segmenting a certain region can be observed for complex segmentation tasks. Deep learning based solution Probabilistic UNet is one of the possible solutions that can learn from a given set of labels for each individual input image and then can produce multiple segmentations for each. But, this does not incorpor...
Vessel Segmentation with deep learning is a challenging task that involves not only learning high-level feature representations but also the spatial continuity of the features across dimensions. Semi-supervised patch-based approaches have been effective in identifying small vessels of 1-2 voxels in diameter but failed to maintain vessel continuity....
Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and, most commonly, medical imaging. Deep learning based techniques have been applied successfully to tackle various complex medical image proce...
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-b...
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating com...
Clustering data is a major task in machine learning. From a user’s perspective, one particular challenge in this area is the differentiation of at least two clusterings. This is especially true when users have to compare clusterings down to the smallest detail. In this paper, we focus on the identification of such clustering differences. We propose...
Public participation processes enable the inclusion of diverse perspectives and allow people to engage in various real-life areas. These processes involve a wide range of data, with a major component being individual contributions that consist primarily of natural language text. Due to the complexity of public participation process data, exploring...
Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities (“modules”) during visual processing and if and...
Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the adv...
Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment fordistinct solid tumour entities. In HT, the tumour tissue is exogenously heated to minimal temperatures of 40 to 41 Cfor 60 minutes. Temperature monitoring can be performed non-invasively using dynamic Magnetic ResonanceImaging (MRI). However,...
Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corre...
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-b...
Dynamic MRI is an essential tool for interventions to visualise movements or changes in the target organ. However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution-also known as the spatio-temporal trade-off. Several approaches, including deep learning based super-resolution approaches, have been proposed t...
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified orig...
Motion artefacts in magnetic resonance images can critically affect diagnosis and the quan-tification of image degradation due to their presence is required. Usually, image quality assessment is carried out by experts such as radiographers, radiologists and researchers. However, subjective evaluation requires time and is strongly dependent on the e...
The possibility of modeling and abstracting interaction has been the key driver in social network-based research as it facilitates, among other things, the generation of recommendations which is vital for most businesses. Being ubiquitous, learning activities also facilitate the formation of these networks. Thus, to gain insights into the evolution...
The efficacy of interventional treatments highly relies on an accurate identification of the target lesions and the interventional tools in the guidance images. Whereas X-ray radiography poses low doses to the patient, its weakness is in the superposition of the different image structures in a 2D image. Cone-beam computed tomography (CBCT) might lo...
Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. If the motion artefacts alter a correct delineation of structure and substructures of the brain, lesions, tumours and so on, the patients need to be re-scanned. Otherwise, neuro-ra...
Deep learning models have shown their potential for several applications. However, most of the models are opaque and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Consequently, creating explainab...
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified orig...
The C-arm Cone-Beam Computed Tomography (CBCT) increasingly plays a major role in interventions and radiotherapy. However, the slow data acquisition and high dose hinder its predominance in the clinical routine. To overcome the high-dose issue, various protocols such as sparse-view have been proposed, where a subset of projections is acquired over...
This paper proposes an extension to the Dual Branch Prior-Net for sparse view interven-tional CBCT reconstruction incorporating a high quality planning scan. An additional head learns to segment interventional instruments and thus guides the reconstruction task. The prior scans are misaligned by up to ±5deg in-plane during training. Experiments sho...
Many deep learning-based techniques have been proposed in recent years to reconstruct undersampled MRI – showing their potential for
shortening the acquisition time. Before using them in actual practice, they are usually evaluated by comparing their results against the available
ground-truth – which is not available during real applications. This r...
Deep learning methods are typically trained in a supervised with annotated data for analysing medical images with the motivation of detecting
pathologies. In the absence of manually annotated training data, unsupervised anomaly detection can be one of the possible solutions. This work
proposes StRegA, an unsupervised anomaly detection pipeline base...
Deep learning pipelines typically require manually annotated training data and the complex reasoning done by such methods make them appear as
“black-boxes” to the end-users, leading to reduced trust. Unsupervised or weakly-supervised techniques could be a possible candidate for solving
the first issue, while explainable classifiers or applying post...
Cartesian sampling techniques are available to speed up the measurement of dynamic MRI, such as k-t GRAPPA. However, radial samplings, such as
iGRASP, are more robust to motion and can be applied for abdominal dynamic MRI. In this work, k-t GRAPPA inspired iGRASP has been created (so called k-t GRASP)–which acquires the subsequent time points by st...
Deep Learning based deformable registration techniques such as Voxelmorph, ICNet, FIRE, do not explicitly encode global dependencies and track
large deformations. This research attempts to encode semantics, i.e. structure and overall view of the anatomy in the supplied image, by
incorporating self-constructing graph network in the latent space of a...
Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and most commonly in medical imaging. Deep Learning based techniques have been applied successfully to tackle various complex medical image proc...
MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly under...
Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning-based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this pap...
Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution -...
Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corre...
Objective:
Hemianopia following occipital stroke is believed to be mainly due to local damage at or near the lesion site. Yet, MRI studies suggest functional connectivity network (FCN) reorganization also in distant brain regions. Because it is unclear if reorganization is adaptive or maladaptive, compensating for, or aggravating vision loss, we c...
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning, and magnetic resonance imaging is the principal imaging modality for diagnosi...
The C-arm Cone-Beam Computed Tomography (CBCT) increasingly plays a major role in interventions and radiotherapy. However, the slow data acquisition and high dose hinder its predominance in the clinical routine. To overcome the high-dose issue, various protocols such as sparse-view have been proposed, where a subset of projections is acquired over...
CT and MRI are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower r...
Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection is an essential part of computer-aided diagnosis, which is still facing great challenges and difficulties to quickly and accurately locate the exact nodules' positions. T...
Stroke is one of the main causes of disability in human beings, and when the occipital lobe is affected, this leads to partial vision loss (homonymous hemianopia). To understand brain mechanisms of vision loss and recovery, graph theory-based brain functional connectivity network (FCN) analysis was recently introduced. However, few brain network st...
Objective: Non-invasive brain stimulation (NIBS) is already known to improve visual field functions in patients with optic nerve damage and partially restores the organization of brain functional connectivity networks (FCNs). However, because little is known if NIBS is effective also following brain damage, we now studied the correlation between vi...
Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection is an essential part of computer-aided diagnosis, which is still facing great challenges and difficulties to quickly and accurately locate the exact nodules' positions. T...
Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this pap...
In this paper, we present a method for removing streak artifacts from reconstructions of sparse cone beam CT (CBCT) projections along circular trajectories. The differentiated backprojection on 2-D planes is combined with convolutional neural networks for both artifact reduction and the ill-posed inversion of the Hilbert transform. Undersampling er...
Accurate nodule location identification is a cornerstone in the diagnostic yield of transbronchial needle biopsy procedures. Due to the overlapping structures, depiction of lung nodules is challenging with chest radiography (CR). While cone-beam computed tomography (CBCT) might provide exact 3D information, its use in real practice is limited by th...
In this paper, we present an approach based on a combination of convolutional neural networks and analytical algorithms to interpolate between neighboring conebeam projections for upsampling along circular trajectories. More precisely, networks are trained to interpolate the angularly centered projection between the input projections of different a...