J
Jordina Torrents-Barrena
Researcher at Pompeu Fabra University
Publications - 10
Citations - 155
Jordina Torrents-Barrena is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Twin-to-twin transfusion syndrome & Autoencoder. The author has an hindex of 4, co-authored 10 publications receiving 66 citations.
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Journal ArticleDOI
Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.
Jordina Torrents-Barrena,Gemma Piella,Narcís Masoller,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel Ángel González Ballester +6 more
TL;DR: This review covers state‐of‐the‐art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time.
Journal ArticleDOI
Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
Jordina Torrents-Barrena,Gemma Piella,Narcís Masoller,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel Ángel González Ballester +6 more
TL;DR: This work proposes a novel fully‐automated method to segment the placenta and its peripheral blood vessels from fetal MRI, and suggests that this methodology can aid the diagnosis and surgical planning of severe fetal disorders.
Journal ArticleDOI
TTTS-GPS: Patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery.
Jordina Torrents-Barrena,Rocío López-Velazco,Gemma Piella,Narcís Masoller,Brenda Valenzuela-Alcaraz,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel Ángel González Ballester +8 more
TL;DR: The proposed TTTS fetal surgery planning and simulation platform is integrated into a flexible C++ and MITK-based application to provide a full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation.
Journal ArticleDOI
Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.
Jordina Torrents-Barrena,Núria Monill,Gemma Piella,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel Ángel González Ballester +6 more
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.
Journal ArticleDOI
Deep Q-CapsNet Reinforcement Learning Framework for Intrauterine Cavity Segmentation in TTTS Fetal Surgery Planning
Jordina Torrents-Barrena,Gemma Piella,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel A. Gonalez Ballester +5 more
TL;DR: This work designs the first automatic approach to detect and segment the intrauterine cavity from axial, sagittal and coronal MRI stacks, and relies on the ability of capsule networks to successfully capture the part-whole interdependency of objects in the scene.