23 citations · 28 across the 8 of their papers we have counts for
7 papers · 1 filter
Automatic 8-tissue Segmentation for 6-month Infant Brains
Yilan Dong, Vanessa Kyriakopoulou, Irina Grigorescu +3
Numerous studies have highlighted that atypical brain development, particularly during infancy and toddlerhood, is linked to an increased likelihood of being diagnosed with a neuro…
Applications of interpretable deep learning in neuroimaging: a comprehensive review
Lindsay Munroe, Mariana da Silva, Faezeh Heidari +5
Clinical adoption of deep learning models has been hindered, in part, because the black-box nature of neural networks leads to concerns regarding their trustworthiness and reliabil…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
Paula Ramirez, Alena Uus, Milou P. M. van Poppel +6
Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in thi…
Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information
Ana Lourenço, Eric Kerfoot, Irina Grigorescu +3
Delayed-enhancement cardiac magnetic resonance (DE-CMR)provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium…
Diffusion tensor driven image registration: a deep learning approach
Irina Grigorescu, Alena Uus, Daan Christiaens +6
Tracking microsctructural changes in the developing brain relies on accurate inter-subject image registration. However, most methods rely on either structural or diffusion data to…