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Found 13 papers, 5 papers with code
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
White matter hyperintensities (WMH) are a hallmark of cerebrovascular disease and multiple sclerosis.
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
A coupled-mechanisms modelling framework for neurodegeneration
We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects.
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks
The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection.
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Computer-aided diagnosis and prediction in brain disorders
Regarding prediction, i. e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer.
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training
To tackle this problem, we propose a framework with four main components: 1) artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, 2) abstract and engineered features to represent images compactly, 3) a feature selection process depending on the artefact class to improve classification, and 4) SVM classifiers to identify artefacts.
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large.
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Recent years have seen increasing use of supervised learning methods for segmentation tasks.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia
To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images.
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.
