Hélène URIEN
Teacher-Researcher, PhD
I obtained my PhD in Signal and Image Processing from Télécom Paris (Paris, France) in 2018. My thesis focused on brain tumor segmentation on PET-MRI images.
I then worked as a research engineer in medical image processing and machine learning applied to MRI images of brain tumors at Neurospin, CEA (Gif-sur-Yvette, France).
I joined Isep in November 2019 as an Associate Professor in Signal and Data Processing.
My research mainly focuses on machine learning and medical image processing.
Search interests:
> Medical Image Processing
> Machine Learning Applied to Medical Images
> Variational and Hierarchical Segmentation
Teaching:
> Signal Processing (APP)
> Data Analysis
> Introduction to Artificial Intelligence
Journals:
- Commowick, Olivier, et al. "Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure." Scientific reports 8.1 (2018): 1-17.
- Le Couedic, Thomas, et al. "Deep-learning based segmentation of challenging myelin sheaths." International Conference on Image Processing Theory, Tools and Applications, IPTA 2020. 2020
- Urien, Hélène, et al. "Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion." International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing. Springer, Cham, 2017
- Urien, Hélène, et al. "A 3D hierarchical multimodal detection and segmentation method for multiple sclerosis lesions in MRI." (2016)
- Urien, Hélène, et al. "3D PET-driven multi-phase segmentation of meningiomas in MRI." 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE, 2016
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