59 Result(s)
within
Chapter
Georg Langs
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Chapter and Conference Paper
Spatio-Temporal Motion Correction and Iterative Reconstruction of In-Utero Fetal fMRI
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses have ...
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Chapter and Conference Paper
Identifying Phenotypic Concepts Discriminating Molecular Breast Cancer Sub-Types
Molecular breast cancer sub-types derived from core-biopsy are central for individual outcome prediction and treatment decisions. Determining sub-types by non-invasive imaging procedures would benefit early as...
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Chapter and Conference Paper
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition
Imaging in clinical routine is subject to changing scanner protocols, hardware, or policies in a typically heterogeneous set of acquisition hardware. Accuracy and reliability of deep learning models suffer fro...
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Chapter and Conference Paper
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI,...
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Chapter and Conference Paper
Dynamic Memory to Alleviate Catastrophic Forgetting in Continuous Learning Settings
In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance. Scanner manufacturer, reconstruction kernel, dose, other protocol specific settings o...
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Chapter
Computer-Assisted Quantification
Computer-aided image analysis and decision support has become an indispensable part of treatment planning in orthopaedic surgery and in osteology. The first use of computers for image interpretation probably w...
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Chapter and Conference Paper
Quantifying Residual Motion Artifacts in Fetal fMRI Data
Fetal functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful tool for investigating brain development in utero, holding promise for generating developmental disease biomarkers and supporting p...
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Chapter and Conference Paper
Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI
Preprocessing and motion correction are essential steps in resting state functional Magnetic Resonance Imaging (rs-fMRI) of the fetal brain. They aim to address the difficult task of removing artefacts caused...
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Chapter and Conference Paper
Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning
The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we pr...
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Chapter and Conference Paper
Predicting Future Bone Infiltration Patterns in Multiple Myeloma
Multiple Myeloma (MM) is a bone marrow malignancy affecting the generation pathway of plasma cells and B-lymphocytes. It results in their uncontrolled proliferation and malignant transformation and ultimately ...
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Chapter
Ethical and Privacy Aspects of Using Medical Image Data
This chapter describes the ethical and privacy aspects of using medical data in the context of the VISCERAL project. The project had as main goals the creation of a benchmark for organ segmentation, landmark d...
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Chapter
Datasets Created in VISCERAL
In the VISCERAL project, several Gold Corpus datasets containing medical imaging data and corresponding manual expert annotations have been created. These datasets were used for training and evaluation of partici...
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Chapter and Conference Paper
Assessing Reorganisation of Functional Connectivity in the Infant Brain
As maturation of neural networks continues throughout childhood, brain lesions insulting immature networks have different impact on brain function than lesions obtained after full network maturation. Thus, lon...
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Chapter
Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark
Health providers currently construct their differential diagnosis for a given medical case most often based on textbook knowledge and clinical experience. Data mining of the large amount of medical records gen...
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Chapter and Conference Paper
Map** Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates
Population level analysis of medical imaging data relies on finding spatial correspondence across individuals as a basis for local comparison of visual characteristics. Here, we describe and evaluate a framewo...
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Chapter and Conference Paper
Multivariate Manifold Modelling of Functional Connectivity in Develo** Language Networks
There is an increasing consensus in the scientific and medical communtities that functional brain analysis should be conducted from a connectionist standpoint. Most connectivity studies to date rely on derived...
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Chapter and Conference Paper
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known mark...
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Chapter
Annotating Medical Image Data
This chapter describes the annotation of the medical image data that were used in the VISCERAL project. Annotation of regions in the 3D images is non-trivial, and tools need to be chosen to limit the manual wo...
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Chapter and Conference Paper
Overview of the 2015 Workshop on Medical Computer Vision — Algorithms for Big Data (MCV 2015)
The 2015 workshop on medical computer vision (MCV): algorithms for big data took place in Munich, Germany, in connection with MICCAI (Medical Image Computing for Computer Assisted Intervention). It is the fift...
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Chapter and Conference Paper
Unsupervised Identification of Clinically Relevant Clusters in Routine Imaging Data
A key question in learning from clinical routine imaging data is whether we can identify coherent patterns that re-occur across a population, and at the same time are linked to clinically relevant patient para...
