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Gemma Piella
Researcher at Pompeu Fabra University
Publications - 158
Citations - 5510
Gemma Piella is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 25, co-authored 143 publications receiving 4411 citations. Previous affiliations of Gemma Piella include Autonomous University of Barcelona & Polytechnic University of Catalonia.
Papers
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Book ChapterDOI
Manifold learning characterization of abnormal myocardial motion patterns: application to CRT-Induced changes
Nicolas Duchateau,Gemma Piella,Adelina Doltra,Lluís Mont,Josep Brugada,Marta Sitges,Bart Bijnens,Mathieu De Craene +7 more
TL;DR: In this article, a method for quantifying the evolution of a given motion pattern under cardiac resynchronization therapy (CRT) was proposed. But the method was not applied to 2D echocardiographic sequences.
Proceedings ArticleDOI
3D fetal face reconstruction from ultrasound imaging
Antònia Alomar,Araceli Morales,Kilian Vellvé,Antonio R. Porras,Fatima Crispi,Marius George Linguraru,Gemma Piella,Federico M. Sukno +7 more
TL;DR: Comunicacio presentada al VISIGRAPP 2021: The 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, celebrat del 8 al 10 de febrer de 2021 de manera virtual.
Proceedings ArticleDOI
Quantification of Oxygen Changes in The Placenta From BOLD MR Image Sequences
TL;DR: This paper proposes a method to track the placenta from a sequence of BOLD MR images acquired under normoxia and hyperoxia conditions with the goal of quantifying how thePlacenta adapts to oxygenation changes and ensures temporal coherence of the tracked structures.
Proceedings ArticleDOI
Quantization of adaptive wavelets for image compression
TL;DR: This paper analyzes the effect of a scalar uniform quantization in an adaptive multiresolution analysis based on a lifting implementation and provides conditions for recovering the original decisions at synthesis.
Book ChapterDOI
Learning and Combining Image Similarities for Neonatal Brain Population Studies
Veronika A. Zimmer,Ben Glocker,Paul Aljabar,Serena J. Counsell,Mary A. Rutherford,A. David Edwards,Jo Hajnal,Miguel Ángel González Ballester,Daniel Rueckert,Gemma Piella +9 more
TL;DR: The utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information and an improved characterization of the resulting embedding is demonstrated.