Jordina Torrents-Barrena

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.

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.

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.

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.

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.