PhD candidate

Department of Biomedical Engineering & Physics
e-mail: j [dot] m [dot] h [dot] noothout [at] amsterdamumc [dot] nl
Phone: +31 20 56 60226
LinkedIn, Google Scholar


 

Julia Noothout obtained her Bachelor of Science degree in Medicine in 2013 from Utrecht University. In 2017 she received her Master of Science degree in Biomedical Image Sciences and with this combination of biomedical training and image processing related research she is able to combine her interest in functionality of the human body and medical imaging.

Her master thesis focused on segmentation of the aortic arch in low-dose chest CT by applying weakly supervised training for convolutional neural networks. In June 2017, Julia started her PhD at the Image Sciences Institute at UMC Utrecht and joined the Quantitative Medical Image Analysis Group. In 2019, she moved with the group to the Amsterdam AMC. Her main research topic is Deep Transfer Learning techniques with an application to cardiac spectral CT.


Journal Articles

1.

J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, E.M. Postma, P.A.M. Smeets, R.A.P. Takx, T. Leiner, M.A. Viergever, I. Išgum

Deep learning-based regression and classification for automatic landmark localization in medical images Journal Article

IEEE Transactions on Medical Imaging, 39 (12), pp. 4011-4022, 2020, ISSN: 1558-254X.

Abstract | Links | BibTeX

Inproceedings

1.

J.M.H. Noothout, E.M. Postma, S. Boesveldt, B.D. de Vos, P.A.M. Smeets, I. Išgum

Automatic segmentation of the olfactory bulbs in MRI Inproceedings

In: SPIE Medical Imaging, pp. 115961J, 2021.

Abstract | Links | BibTeX

2.

L.D. van Harten; J.M.H. Noothout; J.J.C. Verhoeff; J.M. Wolterink; I. Išgum

Automatic segmentation of organs at risk in thoracic CT scans by combining 2D and 3D convolutional neural networks Inproceedings

In: Proc. of SegTHOR challenge at IEEE International Symposium on Biomedical Imaging, 2019.

Abstract | Links | BibTeX

3.

J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, I. Išgum

Automatic segmentation of thoracic aorta segments in low-dose chest CT Inproceedings

In: SPIE Medical Imaging, pp. 105741S, 2018.

Abstract | Links | BibTeX

4.

J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, T. Leiner, I. Išgum

CNN-based Landmark Detection in Cardiac CTA Scans Inproceedings

In: Medical Imaging with Deep Learning (MIDL 2018), 2018.

Abstract | Links | BibTeX

Abstracts

1.

J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, R.A.P. Takx, T. Leiner, I. Išgum

Deep learning for automatic landmark localization in CTA for transcatheter aortic valve implantation Abstract

In: Radiological Society of North America, 105th Annual Meeting, 2019.

Abstract | Links | BibTeX