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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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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

A general framework for multiresolution image fusion: from pixels to regions

Gemma Piella
- 01 Dec 2003 - 
TL;DR: The aim is to reframe the multiresolution-based fusion methodology into a common formalism and to develop a new region-based approach which combines aspects of both object and pixel-level fusion.
Proceedings ArticleDOI

A new quality metric for image fusion

Gemma Piella, +1 more
TL;DR: Three variants of a new quality metric for image fusion based on an image quality index recently introduced by Wang and Bovik are presented, which are compliant with subjective evaluations and can therefore be used to compare different image fusion methods or to find the best parameters for a given fusion algorithm.
Journal ArticleDOI

Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography.

Mathieu De Craene, +10 more
- 01 Feb 2012 - 
TL;DR: TDFFD was applied to a database of cardiac 3D US images of the left ventricle acquired from 9 healthy volunteers and 13 patients treated by Cardiac Resynchronization Therapy (CRT), showing the potential of the proposed algorithm for the assessment of CRT.
Journal ArticleDOI

Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.

Maja Cikes, +14 more
- 01 Jan 2019 - 
TL;DR: This work tested the hypothesis that a machine learning algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).