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Axel Largent, Ph.D.

Research Postdoctoral Fellow

Axel Largent, PhD, is a postdoctoral fellow at the Developing Brain Institute from Children’s National Hospital. His current works are focused on the development of cutting-edge machine learning algorithms (notably deep learning algorithms) for automatic segmentation of fetal and neonatal brains. During his previous postdoctoral fellow and PhD at the LTSI of the University of Rennes (a French INSERM laboratory), Dr. Largent conducted research on MRI-based dose calculation for external beam radiotherapy.


Address: 111 Michigan Ave NW, Washington, DC 20010
Email: fetalbrain@childrensnational.org
Department: MRI Lab

Recent Publications & Presentations

2021
Axel Largent PhD, Kushal Kapse MS, Scott D. Barnett PhD, Josepheen De Asis‐Cruz MD, PhD, Matthew Whitehead MD, Jonathan Murnick MD, PhD, Li Zhao PhD, Nicole Andersen BA, Jessica Quistorff MPH, Catherine Lopez MS, Catherine Limperopoulos PhD
Image Quality Assessment of Fetal Brain MRI Using Multi‐Instance Deep Learning Methods
[published online ahead of print, 2021 Apr 23]. J Magn Reson Imaging 2021 Apr 23. doi: 10.1002/jmri.27649. https://onlinelibrary.wiley.com
2020
Barateau A, De Crevoisier R, Largent A, Mylona E, Perichon N, Castelli J, Chajon E, Acosta O, Simon A, Nunes JC, Lafond, C.
Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning
[published online ahead of print, 2020 Jul 12]. Medical Physics. 2020;10.1002/mp.14387. doi:10.1002/mp.14387 https://aapm.onlinelibrary.wiley.com
2020
Largent A, Marage L, Gicquiau I, Nunes JC, Reynaert N, Castelli J, Chajon E, Acosta O, Gambarota G, de Crevoisier R, Saint-Jalmes H.
Head-and-neck MRI-only radiotherapy treatment planning: From acquisition in treatment position to pseudo-CT generation.
Cancer/Radiothérapie. 2020;24(4):288-297. doi:10.1016/j.canrad.2020.01.008 https://www.sciencedirect.com
2020
Krishnamurthy D, Wu Y, Largent A, Kapse K, Amgalan A, Andescavage N, Zhao L, Limperopoulos C
Automated fetal whole-body MRI segmentation using a 3D U-Net Deep Learning Method
Conference: Pediatric Academic Societies, 2020
2019
Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, Greer PB, Dowling JA, Baxter J, Saint-Jalmes H, Acosta O, de Crevoisier R.
Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning.
International Journal of Radiation Oncology, Biology, Physics. 2019;105(5):1137-1150. doi:10.1016/j.ijrobp.2019.08.049 https://www.sciencedirect.com
2019
Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, Greer PB, Dowling JA, Baxter J, Saint-Jalmes H, Acosta O, de Crevoisier R.
Pseudo-CT generation for MRI-only radiation therapy treatment planning: Comparison among patch-based, atlas-based, and bulk density methods.
International Journal of Radiation Oncology, Biology, Physics. 2019;103(2):479-490. doi:10.1016/j.ijrobp.2018.10.002 https://www.sciencedirect.com
2019
Largent A, Nunes JC, Saint-Jalmes H, Baxter J, Greer P, Dowling J, de Crevoisier R, Acosta O.
Pseudo-CT generation for MRI-only radiotherapy: Comparative study between a generative adversarial network, a U-Net network, a patch-based, and an atlas based methods.
In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 1109-1113). IEEE. April 2019 https://ieeexplore.ieee.org
2017
Largent, A., Nunes, J.C., Saint-Jalmes, H., Simon, A., Perichon, N., Barateau, A., Herve, C., Lafond, C., Greer, P., Dowling, J., De Crevoisier, R. and Acosta, O
Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning
Signal Processing Conference (EUSIPCO), 2017 25th European. IEEE, 2017. p. 46-50. https://ieeexplore.ieee.org