M
Maria A. Zuluaga
Researcher at Institut Eurécom
Publications - 106
Citations - 3011
Maria A. Zuluaga is an academic researcher from Institut Eurécom. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 19, co-authored 92 publications receiving 2001 citations. Previous affiliations of Maria A. Zuluaga include University of Los Andes & University College London.
Papers
More filters
Journal ArticleDOI
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Guotai Wang,Wenqi Li,Maria A. Zuluaga,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: A novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline and proposing a weighted loss function considering network and interaction-based uncertainty for the fine tuning is proposed.
Journal ArticleDOI
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang,Maria A. Zuluaga,Wenqi Li,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: A deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.
Proceedings ArticleDOI
USAD: UnSupervised Anomaly Detection on Multivariate Time Series
TL;DR: A fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders capable of learning in an unsupervised way is proposed.
Journal ArticleDOI
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Guotai Wang,Wenqi Li,Maria A. Zuluaga,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: In this article, the authors proposed a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline.
Journal ArticleDOI
Right ventricle segmentation from cardiac MRI: a collation study.
Caroline Petitjean,Maria A. Zuluaga,Wenjia Bai,Jean-Nicolas Dacher,Damien Grosgeorge,Jérôme Caudron,Su Ruan,Ismail Ben Ayed,M. Jorge Cardoso,Hsiang Chou Chen,Daniel Jimenez-Carretero,Maria J. Ledesma-Carbayo,Christos Davatzikos,Jimit Doshi,Guray Erus,Oskar Maier,Cyrus M. S. Nambakhsh,Yangming Ou,Yangming Ou,Sebastien Ourselin,Chun Wei Peng,Nicholas S. Peters,Terry M. Peters,Martin Rajchl,Daniel Rueckert,Andres Santos,Wenzhe Shi,Ching-Wei Wang,Haiyan Wang,Jing Yuan +29 more
TL;DR: Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden.