Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks. - Daniël Maria Pelt; Kees Joost Batenburg (IEEE Trans. Image Process., 2013)

A Medium-Grain Method for Fast 2D Bipartitioning of Sparse Matrices. - Daniël Maria Pelt; Rob H. Bisseling (IPDPS, 2014)

Improving Filtered Backprojection Reconstruction by Data-Dependent Filtering. - Daniël Maria Pelt; Kees Joost Batenburg (IEEE Trans. Image Process., 2014)

An exact algorithm for sparse matrix bipartitioning. - Daniël Maria Pelt; Rob H. Bisseling (J. Parallel Distributed Comput., 2015)

A method for locally approximating regularized iterative tomographic reconstruction methods. - Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2016)

Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks. - Daniël Maria Pelt; Kees Joost Batenburg; James A. Sethian (J. Imaging, 2018)

Real-time segmentation for tomographic imaging. - Richard Schoonhoven; Jan-Willem Buurlage; Daniël Maria Pelt; Kees Joost Batenburg (MLSP, 2020)

Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging. - Allard A. Hendriksen; Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2020)

LEAN: graph-based pruning for convolutional neural networks by extracting longest chains. - Richard Schoonhoven; Allard A. Hendriksen; Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2020)

A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks. - Marinus J. Lagerwerf; Daniël Maria Pelt; Willem Jan Palenstijn; Kees Joost Batenburg (J. Imaging, 2020)

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. - Mathé T. Zeegers; Daniël Maria Pelt; Tristan van Leeuwen; Robert van Liere; Kees Joost Batenburg (J. Imaging, 2020)

Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. - Allard A. Hendriksen; Daniël Maria Pelt; Kees Joost Batenburg (IEEE Trans. Computational Imaging, 2020)

Improving reproducibility in synchrotron tomography using implementation-adapted filters. - Poulami Somanya Ganguly; Daniël Maria Pelt; Doga Gürsoy; Francesco De Carlo; Kees Joost Batenburg (CoRR, 2021)

An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation. - Serban Vadineanu; Daniël Maria Pelt; Oleh Dzyubachyk; Kees Joost Batenburg (MIDL, 2022)

A tomographic workflow to enable deep learning for X-ray based foreign object detection. - Mathé T. Zeegers; Tristan van Leeuwen; Daniël Maria Pelt; Sophia Bethany Coban; Robert van Liere; Kees Joost Batenburg (Expert Syst. Appl., 2022)

SR4ZCT: Self-supervised Through-Plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. - Jiayang Shi; Daniël Maria Pelt; Kees Joost Batenburg (MLMI@MICCAI (1), 2023)

Multi-stage Deep Learning Artifact Reduction for Computed Tomography. - Jiayang Shi; Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2023)

From Feature Maps to Few-Shot Cell Segmentation. - Serban Vadineanu; Daniël Maria Pelt; Oleh Dzyubachyk; Kees Joost Batenburg (MOVI@MICCAI, 2024)

Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. - Jiayang Shi; Junyi Zhu 002; Daniël Maria Pelt; Kees Joost Batenburg; Matthew B. Blaschko (Trans. Mach. Learn. Res., 2024)

Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. - Serban Vadineanu; Daniël Maria Pelt; Oleh Dzyubachyk; Kees Joost Batenburg (J. Imaging, 2024)