Daniël Maria Pelt

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 18

Top Topics

Sparse Matrices

Cell Segmentation

Top Venues

IEEE Trans. Image Process.

Publications

Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg
SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. CoRR (2024)
Jiayang Shi, Junyi Zhu, Daniël Maria Pelt, Kees Joost Batenburg, Matthew B. Blaschko
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. CoRR (2024)
Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. MILLanD@MICCAI (2023)
Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg
Multi-stage Deep Learning Artifact Reduction for Computed Tomography. CoRR (2023)
Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg
SR4ZCT: Self-supervised Through-Plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. MLMI@MICCAI (1) (2023)
Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg
An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation. MIDL (2022)
Mathé T. Zeegers, Tristan van Leeuwen, Daniël Maria Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg
A tomographic workflow to enable deep learning for X-ray based foreign object detection. Expert Syst. Appl. 206 (2022)
Mathé T. Zeegers, Tristan van Leeuwen, Daniël Maria Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg
A tomographic workflow to enable deep learning for X-ray based foreign object detection. CoRR (2022)
Poulami Somanya Ganguly, Daniël Maria Pelt, Doga Gürsoy, Francesco De Carlo, Kees Joost Batenburg
Improving reproducibility in synchrotron tomography using implementation-adapted filters. CoRR (2021)
Richard Schoonhoven, Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains. CoRR (2020)
Mathé T. Zeegers, Daniël Maria Pelt, Tristan van Leeuwen, Robert van Liere, Kees Joost Batenburg
Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. J. Imaging 6 (12) (2020)
Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg
Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging. CoRR (2020)
Marinus J. Lagerwerf, Daniël Maria Pelt, Willem Jan Palenstijn, Kees Joost Batenburg
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks. CoRR (2020)
Richard Schoonhoven, Jan-Willem Buurlage, Daniël Maria Pelt, Kees Joost Batenburg
Real-time segmentation for tomographic imaging. MLSP (2020)
Marinus J. Lagerwerf, Daniël Maria Pelt, Willem Jan Palenstijn, Kees Joost Batenburg
A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks. J. Imaging 6 (12) (2020)
Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg
Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. IEEE Trans. Computational Imaging 6 (2020)
Daniël Maria Pelt, Kees Joost Batenburg, James A. Sethian
Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks. J. Imaging 4 (11) (2018)
Daniël Maria Pelt, Kees Joost Batenburg
A method for locally approximating regularized iterative tomographic reconstruction methods. CoRR (2016)
Daniël Maria Pelt, Rob H. Bisseling
An exact algorithm for sparse matrix bipartitioning. J. Parallel Distributed Comput. 85 (2015)
Daniël Maria Pelt, Rob H. Bisseling
A Medium-Grain Method for Fast 2D Bipartitioning of Sparse Matrices. IPDPS (2014)
Daniël Maria Pelt, Kees Joost Batenburg
Improving Filtered Backprojection Reconstruction by Data-Dependent Filtering. IEEE Trans. Image Process. 23 (11) (2014)
Daniël Maria Pelt, Kees Joost Batenburg
Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks. IEEE Trans. Image Process. 22 (12) (2013)