58 citations · 58 across the 3 of their papers we have counts for
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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.…