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Robust-ComBat: Mitigating Outlier Effects in Diffusion MRI Data Harmonization
Yoan David, Pierre-Marc Jodoin, Alzheimer's Disease Neuroimaging Initiative +1
Harmonization methods such as ComBat and its variants are widely used to mitigate diffusion MRI (dMRI) site-specific biases. However, ComBat assumes that subject distributions exhi…
Clinical-ComBAT: a diffusion-weighted MRI harmonization method for clinical applications
Gabriel Girard, Manon Edde, Félix Dumais +7
Diffusion-weighted magnetic resonance imaging (DW-MRI) derived scalar maps are effective for assessing neurodegenerative diseases and microstructural properties of white matter in…
Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning
Anders Austlid Taskén, Thierry Judge, Erik Andreas Rye Berg +8
Segmental longitudinal strain (SLS) of the left ventricle (LV) is an important prognostic indicator for evaluating regional LV dysfunction, in particular for diagnosing and managin…
Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling
Thierry Judge, Olivier Bernard, Woo-Jin Cho Kim +5
Echocardiography plays a fundamental role in the extraction of clinical parameters (e.g. left ventricular volume and ejection fraction) required to determine the presence and sever…
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Nathan Painchaud, Jérémie Stym-Popper, Pierre-Yves Courand +4
Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain. These descriptors provi…
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering
Daniel Jörgens, Pierre-Marc Jodoin, Maxime Descoteaux +1
One of the main issues of the current tractography methods is their high false-positive rate. Tractogram filtering is an option to remove false-positive streamlines from tractograp…