Publications by Laura Ioana Gui

Amygdala volumes and associations with socio-emotional competencies in preterm youth: cross-sectional and longitudinal data

Elda Fischi Gomez, Vanessa Siffredi, Laura Ioana Gui, Petra Susan Hüppi

Background Socio-emotional difficulties often result from very preterm (VPT) birth. The amygdala's developmental trajectory, including its nuclei, has been recognized as a significant factor in observed difficulties. This study aims to assess the relations ...

Springernature2024

Behavioral outcome of very preterm children at 5 years of age: Prognostic utility of brain tissue volumes at term-equivalent-age, perinatal, and environmental factors

Djalel Eddine Meskaldji, Laura Ioana Gui, Serafeim Loukas

ObjectivePrematurity is associated with a high risk of long-term behavioral problems. This study aimed to assess the prognostic utility of volumetric brain data at term-equivalent-age (TEA), clinical perinatal factors, and parental social economic risk in ...

WILEY2023

Music enhances structural maturation of emotional processing neural pathways in very preterm infants

Nicolas Kunz, Laura Ioana Gui, François Lazeyras

Prematurity disrupts brain maturation by exposing the developing brain to different noxious stimuli present in the neonatal intensive care unit (NICU) and depriving it from meaningful sensory inputs during a critical period of brain development, leading to ...

2020

Longitudinal study of neonatal brain tissue volumes in preterm infants and their ability to predict neurodevelopmental outcome

Djalel Eddine Meskaldji, Laura Ioana Gui, Serafeim Loukas

Premature birth has been associated with poor neurodevelopmental outcomes. However, the relation between such outcomes and brain growth in the neonatal period has not yet been fully elucidated. This study investigates longitudinal brain development between ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge

Elda Fischi Gomez, Laura Ioana Gui

A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born ...

Elsevier Science Bv2015

Morphology-driven automatic segmentation of MR images of the neonatal brain

Michel Kocher, Laura Ioana Gui, François Lazeyras, Radoslaw Lisowski

The segmentation of MR images of the neonatal brain is an essential step in the study and evaluation of infant brain development. State-of-the-art methods for adult brain MRI segmentation are not applicable to the neonatal brain, due to large differences i ...

Elsevier Science Bv2012

Cooperative Object Segmentation and Behavior Inference in Image Sequences

Jean-Philippe Thiran, Laura Ioana Gui

In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior inference over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to eac ...

2009

A collaborative approach to image segmentation and behavior recognition from image sequences

Laura Ioana Gui

Visual behavior recognition is currently a highly active research area. This is due both to the scientific challenge posed by the complexity of the task, and to the growing interest in its applications, such as automated visual surveillance, human-computer ...

EPFL2008

Finger-spelling Recognition within a Collaborative Segmentation/Behavior Inference Framework

Jean-Philippe Thiran, Laura Ioana Gui

We introduce a new approach for finger-spelling recognition from video sequences, relying on the collaboration between the feature extraction and behavior inference processes. The inference process dynamically guides the segmentation- based feature extract ...

2008

A Variational Framework for the Simultaneous Segmentation and Object Behavior Classification of Image Sequences

Jean-Philippe Thiran, Laura Ioana Gui

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carrie ...

2007