Search Results for author:
Found 34 papers, 19 papers with code
ISLES 2024: The first longitudinal multimodal multi-center real-world dataset in (sub-)acute stroke
In contrast, our dataset is the first to offer comprehensive longitudinal stroke data, including acute CT imaging with angiography and perfusion, follow-up MRI at 2-9 days, as well as acute and longitudinal clinical data up to a three-month outcome.
ISLES'24: Improving final infarct prediction in ischemic stroke using multimodal imaging and clinical data
This work presents the ISLES'24 challenge, which addresses final post-treatment stroke infarct prediction from pre-interventional acute stroke imaging and clinical data.
DWARF: Disease-weighted network for attention map refinement
The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations.
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis
The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Deep Learning-based Type Identification of Volumetric MRI Sequences
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Combining unsupervised and supervised learning for predicting the final stroke lesion
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention.
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation
DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness.
pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines.
Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs).
Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning
We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI.
Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting
Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction.
Dual-Stream Pyramid Registration Network
We propose a Dual-Stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration.
Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation
Therefore, we report results of evaluating common voxel-wise uncertainty measures with respect to their reliability, and limitations on two medical image segmentation datasets.
Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
By synthetic experiments, we further show the capability of our approach in learning an explicit anatomical shape representation.
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity.
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Instead, we propose a method for identifying lesion changes of high certainty, and establish on a dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0. 99), while changes in lesion volume are much less able to perform this separation (AUC = 0. 71).
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned.
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN).
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown promising results.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
In this way, we overcome the need for expert annotations of regions of interest.
Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition.
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity.
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life.
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model.
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e. g., neurosurgical interventions, radiotherapy planning), where automated medical image segmentation is crucial.
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
This paper proposes a novel approach for uncertainty quantification in dense Conditional Random Fields (CRFs).
