G
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
Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding.
Oualid Benkarim,Gerard Sanroma,Gemma Piella,Islem Rekik,N.M. Hahner,Elisenda Eixarch,Miguel Ángel González Ballester,Dinggang Shen,Gang Li +8 more
TL;DR: A novel method to identify spatially fine-scaled association maps between cortical development and VM by leveraging vertex-wise correlations between the growth patterns of both ventricular and cortical surfaces in terms of area expansion and curvature information is developed.
Proceedings ArticleDOI
Endocardial motion estimation from electro-anatomical data
Antonio R. Porras,Gemma Piella,Corné Hoogendoorn,David Andreu,Antonio Berruezo,Alejandro F. Frangi +5 more
TL;DR: A method to reconstruct the endocardial motion intra-operatively by exploiting both information from an electro-anatomical mapping system and the heart anatomy segmented from any pre-operative 3D imaging modality, using a bilinear statistical atlas to approximate motion in the areas where no information is provided is proposed.
Proceedings ArticleDOI
Fetal cortical parcellation based on growth patterns
Jing Xia,Caiming Zhang,Fan Wang,Oualid Benkarim,Gerard Sanroma,Gemma Piella,Miguel A. Gonzalez Balleste,N.M. Hahner,Elisenda Eixarch,Dinggang Shen,Gang Li +10 more
TL;DR: A novel method to divide a population of fetal cortical surfaces into distinct regions based on the dynamic growth patterns of cortical properties, which indicate the underlying changes of microstructures is proposed.
Journal ArticleDOI
Iterated random walks with shape prior
TL;DR: A new framework for image segmentation using random walks where a distance shape prior is combined with a region term and the region term is computed with k-means to estimate the parametric probability density function.
Book ChapterDOI
Statistical Shape Model with Random Walks for Inner Ear Segmentation
TL;DR: A new framework for segmentation of micro-CT cochlear images using random walks combined with a statistical shape model (SSM) allows us to constrain the less contrasted areas and ensures valid inner ear shape outputs.