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Found 24 papers, 14 papers with code
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).
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.
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging.
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.
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.
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown promising results.
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).
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
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.
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).
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.
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.
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by separated training on low-grade gliomas (LGGs) and high-grade gliomas (HGGs), but in practice this information is not available at test time to decide which model to use.
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.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
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.
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.
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.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
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.
