M. V. Eijnatten

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

Kees Joost Batenburg, Maureen van Eijnatten, Peter Maass, 2021, J. Imaging.

Challenges in medical additive manufacturing

M.A.J.M. van Eijnatten, M. V. Eijnatten, 2017 .

CT image segmentation of bone for medical additive manufacturing using a convolutional neural network

Wouter M. Kouw, Jordi Minnema, Maureen van Eijnatten, 2018, Comput. Biol. Medicine.

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning pdf

Kees Joost Batenburg, Carola-Bibiane Schönlieb, Maureen van Eijnatten, 2020, Comput. Methods Programs Biomed..

Influence of CT parameters on STL model accuracy

Maureen van Eijnatten, Jan Wolff, Pim de Graaf, 2017 .

Comparison of convolutional neural network training strategies for cone-beam CT image segmentation

Kees Joost Batenburg, Jordi Minnema, Maureen van Eijnatten, 2021, Comput. Methods Programs Biomed..

Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning pdf

Kees Joost Batenburg, Maureen van Eijnatten, Sophia Bethany Coban, 2020, ArXiv.

A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning pdf

Kees Joost Batenburg, Maureen van Eijnatten, Henri Der Sarkissian, 2019, ArXiv.

The impact of manual threshold selection in medical additive manufacturing

Maureen van Eijnatten, Jan Wolff, Juha Koivisto, 2016, International Journal of Computer Assisted Radiology and Surgery.

CT image segmentation for additive manufactured skull implants using deep learning

Kees Joost Batenburg, Jordi Minnema, Maureen van Eijnatten, 2019 .