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Elodie Puybareau

Researcher at École Pour l'Informatique et les Techniques Avancées

Publications -  38
Citations -  2140

Elodie Puybareau is an academic researcher from École Pour l'Informatique et les Techniques Avancées. The author has contributed to research in topics: Segmentation & Computer science. The author has an hindex of 11, co-authored 33 publications receiving 1224 citations. Previous affiliations of Elodie Puybareau include University of Paris & French Institute of Health and Medical Research.

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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.
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Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Hugo J. Kuijf, +43 more
- 19 Mar 2019 - 
TL;DR: There is a cluster of four methods that rank significantly better than the other methods, with one clear winner, and the inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners.
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Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge

Li Wang, +28 more
- 27 Feb 2019 - 
TL;DR: The iSeg-2017 challenge provides a set of six-month infant subjects with manual labels for training and testing the participating methods, and among the 21 automatic segmentation methods participating, the eight top-ranked teams are reviewed, in terms of Dice ratio, modified Hausdorff distance, and average surface distance.
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Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Hugo J. Kuijf, +43 more
- 01 Apr 2019 - 
TL;DR: The WMH segmentation challenge as discussed by the authors was the first attempt to evaluate the performance of automatic segmentation of cerebral white matter hyperintensities (WMH) of presumed vascular origin.
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A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

Zhaohan Xiong, +43 more
- 01 Jan 2021 - 
TL;DR: This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.