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Elisenda Eixarch

Researcher at University of Barcelona

Publications -  188
Citations -  5280

Elisenda Eixarch is an academic researcher from University of Barcelona. The author has contributed to research in topics: Medicine & Gestational age. The author has an hindex of 32, co-authored 152 publications receiving 4297 citations. Previous affiliations of Elisenda Eixarch include Imperial College London & Hospital Sant Joan de Déu Barcelona.

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Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation.

TL;DR: Two ensembling strategies are explored, namely, stacking and cascading to combine the strengths of both families, and results show that either combination strategy outperform all of the individual methods, thus demonstrating the capability of learning systematic combinations that lead to an overall improvement.
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Rate and Outcomes of Pulmonary Stenosis and Functional Pulmonary Atresia in Recipient Twins with Twin-Twin Transfusion Syndrome

TL;DR: Pregnancies with recipient twins with PS/PA had lower survival of at least one twin and lower overall survival at 6 months of age and about one third showed persistence of pulmonary valve pathology after delivery, which stresses the need for strict follow up.
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Clinical and biochemical predictors of very preterm birth in twin-to-twin transfusion syndrome treated by fetoscopy

TL;DR: A single survivor after surgery was a strong protective factor of very preterm birth and inflammatory biomarkers and duration of surgery did not discriminate risk of prematurity.
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Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.

TL;DR: This work aims to efficiently segment different intrauterine tissues in fetal magnetic resonance imaging (MRI) and 3D ultrasound and suggests that combining the selected 10 radiomic features per anatomy along with DeepLabV3+ or BiSeNet architectures for MRI, and PSPNet or Tiramisu for 3D US, can lead to the highest fetal / maternal tissue segmentation performance, robustness, informativeness, and heterogeneity.