58 citations · 58 across the 3 of their papers we have counts for
4 papers
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images
Yanna Cruz Cavalcanti, Thomas Oberlin, Vinicius Ferraris +3
When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Fact…
Factor analysis of dynamic PET images: beyond Gaussian noise
Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon +4
Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data.…
Unmixing dynamic PET images with variable specific binding kinetics
Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon +3
To analyze dynamic positron emission tomography (PET) images, various generic multivariate data analysis techniques have been considered in the literature, such as principal compon…