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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.

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Proceedings ArticleDOI

Myocardial deformation from tagged MRI in hypertrophic cardiomyopathy using an efficient registration strategy

TL;DR: This paper combines different parallelization strategies for speeding up motion and deformation computation by non-rigid registration of a sequence of images to compare strain in healthy subjects and hypertrophic cardiomyopathy (HCM) patients.
Journal ArticleDOI

TTTS-STgan: Stacked Generative Adversarial Networks for TTTS Fetal Surgery Planning Based on 3D Ultrasound

TL;DR: A novel multi-task stacked generative adversarial framework is proposed to jointly learn synthetic fetal US generation, multi-class segmentation of the placenta, its inner acoustic shadows and peripheral vasculature, andplacenta shadowing removal and could be implemented in a TTTS fetal surgery planning software.
Journal ArticleDOI

Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments

TL;DR: In this paper , a network-based model at the chondrocyte level was proposed to enable the semiquantitative interpretation of the intricate mechanisms of osteoarthritis progression, incorporating the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals.
Proceedings Article

A variational approach for image fusion visualization

TL;DR: A variational framework to perform the fusion of an arbitrary number of images while preserving the salient information and enhancing the contrast for visualization is proposed.
Proceedings ArticleDOI

Fetal MRI Synthesis via Balanced Auto-Encoder Based Generative Adversarial Networks

TL;DR: An auto-encoder based Generative Adversarial Network is adopted for synthetic fetal MRI generation that features a balanced power of the discriminator against the generator during training, provides an approximate convergence measure, and enables fast and robust training to generate high-quality fetal MRI in axial, sagittal and coronal planes.