O
Oula Puonti
Researcher at Copenhagen University Hospital
Publications - 46
Citations - 966
Oula Puonti is an academic researcher from Copenhagen University Hospital. The author has contributed to research in topics: Brain stimulation & Medicine. The author has an hindex of 11, co-authored 31 publications receiving 518 citations. Previous affiliations of Oula Puonti include Technical University of Denmark.
Papers
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Journal ArticleDOI
Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art.
Jesper Duemose Nielsen,Kristoffer Hougaard Madsen,Oula Puonti,Hartwig R. Siebner,Christian Bauer,Camilla Gøbel Madsen,Guilherme B. Saturnino,Axel Thielscher,Axel Thielscher +8 more
TL;DR: Three methods for skull segmentation are evaluated, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite, to rigorously assess their accuracy by comparison with CT‐based skull segmentations on a group of ten subjects.
Book ChapterDOI
SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation
Guilherme B. Saturnino,Guilherme B. Saturnino,Oula Puonti,Jesper Duemose Nielsen,Jesper Duemose Nielsen,Daria Antonenko,Kristoffer Hougaard Madsen,Kristoffer Hougaard Madsen,Axel Thielscher,Axel Thielscher +9 more
TL;DR: The SimNIBS (Simulation of NIBS) software package is introduced, providing easy-to-use automated tools for electric field modelling, and an overview of the modelling pipeline is given, with step-by-step examples of how to run a simulation.
Journal ArticleDOI
Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling.
TL;DR: The performance of a segmentation algorithm designed to meet requirements of quantitative analysis of magnetic resonance imaging scans of the brain is validated, building upon generative parametric models previously used in tissue classification.
Book ChapterDOI
An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation
TL;DR: This paper proposes a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images and shows improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation without growcut.
Journal ArticleDOI
Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.
Oula Puonti,Oula Puonti,Koen Van Leemput,Koen Van Leemput,Guilherme B. Saturnino,Guilherme B. Saturnino,Hartwig R. Siebner,Hartwig R. Siebner,Kristoffer Hougaard Madsen,Kristoffer Hougaard Madsen,Axel Thielscher,Axel Thielscher +11 more
TL;DR: A new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans is presented, which compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues.