scispace - formally typeset
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
More filters
Journal ArticleDOI

A novel approach to multiple anatomical shape analysis: Application to fetal ventriculomegaly

TL;DR: This work proposes a novel approach to identify fine-grained associations between cortical folding and ventricular enlargement by leveraging the vertex-wise correlations between their growth patterns in terms of area expansion and curvature, and reveals clinically relevant and heterogeneous regional associations.
Journal ArticleDOI

Improved myocardial motion estimation combining tissue Doppler and B-mode echocardiographic images.

TL;DR: Results show that the proposed technique for myocardial motion estimation based on image registration using both B-mode echocardiographic images and tissue Doppler sequences acquired interleaved provides a robust motion estimate in these situations.
Journal ArticleDOI

Atlas-based quantification of myocardial motion abnormalities: added-value for understanding the effect of cardiac resynchronization therapy.

TL;DR: An atlas of normal septal motion built using apical four-chamber two-dimensional echocardiographic sequences from healthy volunteers with 88 patients undergoing CRT at baseline and at 12 months follow-up to demonstrate the clinical value of such a method.
Proceedings ArticleDOI

A Three-Step Nonlinear Lifting Scheme for Lossless Image Compression

TL;DR: An adaptive wavelet transform is proposed that results in fewer large detail coefficients while preserving image contours in the approximation subband using a three-step nonlinear lifting scheme.
Book ChapterDOI

Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation

TL;DR: This work introduces the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation.