Daniël M. Pelt

Employment

2020 - now Assistant Professor

2017 - 2020 Postdoctoral Researcher

2016 - 2017 Postdoctoral Fellow

2012 - 2016 Ph.D. Student

2010 - 2011 Scientific Programmer

Education

2016 Ph. D.

2010 Master Scientific Computing (cum laude)

2008 Bachelor Physics and Astronomy

2004 Pre-university secondary education

Academic activities

Scholarships/prizes

2019 NWO Smart solutions for horti- and agriculture 2019 (consortium partner, 800k euro): Universal Three-dimensiOnal Passport for process Individualization in Agriculture

EU ATTRACT (co-I, 100k euro): Combining cycloidal computed tomography with machine learning

2018 NWO Veni Grant (PI, 250k euro): Machine learning for large 3D tomographic images

2017 IOP Outstanding Reviewer Award

2015 Research Aide Appointment (PI, 4800 dollar), Argonne National Laboratory

2014 Short Term Scientific Mission (PI, 1100 euro), EU COST Action MP-1207

Membership

2020 - now Topic Editor, Jounal of Imaging

Ad hoc reviewer

Scientific Reports, eLife, IEEE Transactions on Image Processing, IEEE Transactions on Compuational Imaging, Journal of Synchrotron Radiation, Measurement Science and Technology, ...

Talks

01/2022 Online, Electronic Imaging Symposium 2022 (invited).

12/2021 Tampere, Finland, Inverse Days 2021 (invited).

05/2021 Milan, Italy, Meeting on Tomography and Applications (panel speaker).

04/2021 Berkeley CA, USA, Workshop on Autonomous Discovery in Science and Engineering (invited).

01/2020 Kanazawa, Japan, The 1st International Conference on Big Data and Machine Learning in Microscopy (invited).

10/2019 Zeist, The Netherlands, The 44th Dutch-Flemish Scientific Computing Society Woudschoten Conference (invited).

08/2019 Lombard IL, USA, The 25th International Congress on X-ray Optics and Microanalysis (invited).

06/2019 Amsterdam, The Netherlands, Code Sprint: Deep Learning for High Resolution 3D Tomographic Reconstruction.

05/2019 Milan, Italy, Meeting on Tomography and Applications (invited).

04/2019 Lund, Sweden, Inverse problems in X-ray phase retrieval and tomography (invited).

11/2018 Berkeley CA, USA, CAMERA Workshop Algorithms and Software for Tomographic Reconstruction for Beamlines.

04/2018 Groningen, The Netherlands, Imaging Informatics Colloquium.

01/2018 Villigen, Switzerland, Meeting at Swiss Light Source, Paul Scherrer Institute.

11/2017 Berkeley CA, USA, CAMERA Workshop Algorithms and Software for Tomographic Reconstruction for Beamlines.

11/2016 Berkeley CA, USA, CAMERA Workshop: Algorithms for Tomographic Reconstruction: State-of-the-Art and Future Goals.

10/2016 Berkeley CA, USA, Advanced Light Source User Meeting 2016.

05/2016 Argonne IL, USA, 2016 APS/CNM Users Meeting 2016 (invited).

02/2016 Berkeley CA, USA, Advanced Light Source Tomography Beamline Seminar.

11/2015 Antwerp, Belgium, International Workshop on Industrial Tomography 2015 (invited).

09/2015 London, UK, Focused Mini-Workshop on Differential Phase Contrast Tomography (invited).

06/2015 Argonne IL, USA, Laboratory for Advanced Numerical Simulations(LANS) Informal Seminar, Mathematics & Computer Science (MCS) Division, Argonne National Laboratory.

06/2015 Argonne IL, USA, Coffee seminar of the Imaging and Microscopy Groups, Advanced Photon Source, Argonne National Laboratory.

06/2015 Newport RI, USA, the 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine.

05/2015 Grenoble, France, Workshop on Experimental and Computational Tomography (invited).

04/2015 Milan, Italy, Meeting on Tomography and Applications (invited).

12/2014 Trieste, Italy, Advances in X-ray Imaging workshop (invited).

11/2014 Antwerp, Belgium, Meeting at Electron microscopy for materials science (EMAT), University of Antwerp.

05/2014 Phoenix AZ, USA, 28th IEEE International Parallel & Distributed Processing Symposium.

05/2014 Hong Kong, China, SIAM Conference on Imaging Science.

03/2014 Villigen, Switzerland, Meeting at Swiss Light Source, Paul Scherrer Institute.

02/2014 Antwerp, Belgium, Meeting at iMinds - Vision Lab, University of Antwerp.

12/2013 Antwerp, Belgium, Meeting at Electron microscopy for materials science (EMAT), University of Antwerp.

07/2013 Ghent, Belgium, 1st International Conference on Tomography of Materials and Structures.

05/2013 Grenoble, France, ESRF SciSoft coffee meeting.

12/2012 Paris, France, Workshop on X-ray tomography reconstruction.

(Co-)Organizer

2019 The 2019/2020 TomoChallenge.

The 2019 CAMERA Workshop: Algorithms and Software for Tomographic Reconstruction for Beamlines.

2018 The 2018 CAMERA Workshop: Algorithms and Software for Tomographic Reconstruction for Beamlines.

2017 The 2017 CAMERA Workshop: Algorithms and Software for Tomographic Reconstruction for Beamlines.

2016 The 2016 CAMERA Workshop: Algorithms for Tomographic Reconstruction: State-of-the-Art and Future Goals.

Working visits

01/2018 Swiss Light Source, Paul Sherrer Institute, Villigen, Switzerland. One week.

06/2015 Advanced Photon Source, Argonne National Laboratory, Argonne, Illinois, USA. Five weeks.

03/2014 Swiss Light Source, Paul Sherrer Institute, Villigen, Switzerland. Two weeks.

05/2013 European Synchrotron Radiation Facility, Grenoble, France. Two weeks.

Teaching

Lecturer for the 2022 Computational Imaging and Tomography course at Leiden University (Master).

Lecturer for the 2022 Introduction to Reinforcement Learning course at Leiden University (Bachelor).

Guest lecturer for the 2015 Parallel Algorithms course, taught by prof. dr. Rob H. Bisseling at Utrecht University.

Teacher at the ASTRA toolbox session during "Workshop on Experimental and Computational Tomography" (May 2015, Grenoble, France).

Teacher at the "Training school on using the ASTRA toolbox for X-ray tomography" (March 2015, Antwerp, Belgium).

Teacher at the "Unleashing the ASTRA Tomography Toolbox" workshop (April 2014, Antwerp, Belgium).

Publication list

2022

Ouyang, R., Costa, A. R., Cassidy, C. K., ... & Briegel, A. (2022). High-resolution reconstruction of a Jumbo-bacteriophage infecting capsulated bacteria using hyperbranched tail fibers. Nature Communications, 13, 7241.

Sieverts, M., Obata, Y., Rosenberg, J. L., Woolley, W., Parkinson, D. Y., Barnard, H. S., ... & Acevedo, C. (2022). Unraveling the effect of collagen damage on bone fracture using in situ synchrotron microtomography with deep learning. Nature Communications Materials, 3(1), 1-13.

Segev-Zarko, L. A., Dahlberg, P. D., Sun, S. Y., Pelt, D. M., Kim, C. Y., Egan, E. S., ... & Boothroyd, J. C. (2022). Cryo-electron tomography with mixed-scale dense neural networks reveals key steps in deployment of Toxoplasma invasion machinery. PNAS nexus, 1(4), pgac183.

Shiraz, A., Egawa, N., Pelt, D. M., Crawford, R., Nicholas, A. K., Romashova, V., ... & Doorbar, J. (2022). Cervical cell lift: A novel triage method for the spatial mapping and grading of precancerous cervical lesions. EBioMedicine, 82, 104157.

Kim, J., Pelt, D. M., Kagias, M., Stampanoni, M., Batenburg, K. J., & Marone, F. (2022). Tomographic reconstruction of the small-angle x-ray scattering tensor with filtered back projection. Physical Review Applied, 18(1), 014043.

Zeegers, M. T., van Leeuwen, T., Pelt, D. M., Coban, S. B., van Liere, R., Batenburg, K.J. (2022). A tomographic workflow to enable deep learning for X-ray based foreign object detection. Expert Systems with Applications, 206, 117768.

Vădineanu, Ș., Pelt, D. M., Dzyubachyk, O., & Batenburg, J. (2022). An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation. In Medical Imaging with Deep Learning 2022.

Pelt, D. M., Roche i Morgó, O., Maughan Jones, C., Olivo, A., & Hagen, C. K. (2022). Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data. Scientific Reports, 12(1), 1-13.

Pelt, D. M., Hendriksen, A. A., & Batenburg, K. J. (2022). Foam-like phantoms for comparing tomography algorithms. Journal of Synchrotron Radiation, 29(1).

2021

Hendriksen, A. A. et. al. (2021). Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python. Optics Express, 29(24), 40494-40513.

Ganguly, P. S., Pelt, D. M., Gürsoy, D., De Carlo, F., & Batenburg, K. J. (2021). Improving reproducibility in synchrotron tomography using implementation-adapted filters. Journal of Synchrotron Radiation 28(5), 1583-1597

Roche i Morgó, O. et. al. (2021). Exploring the potential of cycloidal computed tomography for advancing intraoperative specimen imaging. Developments in X-Ray Tomography XIII. Vol. 11840. International Society for Optics and Photonics, 2021.

Skorikov, A., Heyvaert, W., Albecht, W., Pelt, D. M., & Bals, S. (2021). Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles. Nanoscale.

Hendriksen, A. A., Bührer, M., Leone, L., Merlini, M., Vigano, N., et al. (2021). Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data. Scientific Reports, 11(1), 11895.

2020

Pelt, D. M. (2020). Deep Learning: Tackling the challenges of bioimage analysis. Elife, 9, e64384.

Lagerwerf, M. J., Pelt, D. M., Palenstijn, W. J., & Batenburg, K. J. (2020). A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks. Journal of Imaging, 6(12), 135.

Zeegers, M. T., Pelt, D. M., van Leeuwen, T., van Liere, R., & Batenburg, K. J. (2020). Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. Journal of Imaging, 6(12), 132.

Pelt, D., Maughan-Jones, C., Morgo, R. I., Olivo, A., & Hagen, D. (2020). Rapid and flexible high-resolution scanning enabled by cycloidal computed tomography and convolutional neural network (CNN) based data recovery. In 6th International Conference on Image Formation in X-Ray Computed Tomography.

Schoonhoven, R., Buurlage, J. W., Pelt, D. M., & Batenburg, K. J. (2020). Real-time segmentation for tomographic imaging. In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 1-6.

Hendriksen, A. A., Pelt, D. M., & Batenburg, K. J. (2020). Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. IEEE Transactions on Computational Imaging, 6, 1320-1335.

Flenner, S. et. al. (2020). Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments. Journal of Synchrotron Radiation, 27(5).

Vanrompay, H. et. al. (2020). Real‐Time Reconstruction of Arbitrary Slices for Quantitative and In Situ 3D Characterization of Nanoparticles. Particle & Particle Systems Characterization, 2000073.

Parkinson, D. Y. et al. (2020). Tomographic Reconstruction for Synchrotron Tomography. In Handbook on Big Data and Machine Learning in the Physical Sciences, 65-82.

Chang, H. et al. (2020). Building Mathematics, Algorithms, and Software for Experimental Facilities. In Handbook on Big Data and Machine Learning in the Physical Sciences, 189-240.

2019 Buurlage, J. W., Marone, F., Pelt, D. M., Palenstijn, W. J., Stampanoni, M., Batenburg, K. J., & Schlepütz, C. M. (2019). Real-time reconstruction and visualisation towards dynamic feedback control during time-resolved tomography experiments at TOMCAT. Scientific Reports, 9(1), 1-11.

Minnema, J. et al. (2019). Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network. Medical Physics.

Hendriksen, A. A., Pelt, D. M., Palenstijn, W. J., Coban, S. B., & Batenburg, K. J. (2019). On-the-Fly Machine Learning for Improving Image Resolution in Tomography. Applied Sciences, 9(12), 2445.

2018 Pelt, D. M., Batenburg, K. J., & Sethian, J. A. (2018). Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks. Journal of Imaging, 4(11), 128.

Parkinson, D. Y. et al. (2018). Achieving fast high-resolution 3D imaging by combining synchrotron x-ray microCT, advanced algorithms, and high performance data management. In Image Sensing Technologies: Materials, Devices, Systems, and Applications V (Vol. 10656, p. 106560S).

Pelt, D. M., & Parkinson, D. Y. (2018). Ring artifact reduction in synchrotron x-ray tomography through helical acquisition. Measurement Science and Technology, 29(3), 034002.

De Carlo, F. et al. (2018). TomoBank: a tomographic data repository for computational x-ray science. Measurement Science and Technology, 29(3), 034004.

Pelt, D. M., & Sethian, J. A. (2018). A mixed-scale dense convolutional neural network for image analysis. Proceedings of the National Academy of Sciences, 115(2), 254-259.

2017 Parkinson, D. Y. et al. (2017). Machine learning for micro-tomography. In Developments in X-Ray Tomography XI (Vol. 10391, p. 103910J).

Perciano, T. et al. (2017). Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics. Journal of synchrotron radiation, 24(5), 1065-1077.

Pelt, D. M., & Andrade, V. (2017). Improved tomographic reconstruction of large-scale real-world data by filter optimization. Advanced Structural and Chemical Imaging, 2(1), 17.

2016 Pelt, D. M. (2016). Filter-based reconstruction methods for tomography (Doctoral dissertation).

Pelt, D. M., Gürsoy, D., Palenstijn, W. J., Sijbers, J., De Carlo, F., & Batenburg, K. J. (2016). Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data. Journal of synchrotron radiation, 23(3), 842-849.

2015 Bladt, E., Pelt, D. M., Bals, S., & Batenburg, K. J. (2015). Electron tomography based on highly limited data using a neural network reconstruction technique. Ultramicroscopy, 158, 81-88.

Pelt, D. M., & Bisseling, R. H. (2015). An exact algorithm for sparse matrix bipartitioning. Journal of Parallel and Distributed Computing, 85, 79-90.

Janssens, E., Pelt, D. M., De Beenhouwer, J., Van Dael, M., Verboven, P., Nicolai, B., & Sijbers, J. (2015). Fast neural network based X-ray tomography of fruit on a conveyor belt. In Frutic Italy 2015: 9th nut and vegetable production engineering symposium (Vol. 44, pp. 181-186).

Pelt, D. M., & Batenburg, K. J. (2015). Accurately approximating algebraic tomographic reconstruction by filtered backprojection. In Proceedings of The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (pp. 158-161).

2014 Pelt, D. M., & Batenburg, K. J. (2014). Improving filtered backprojection reconstruction by data-dependent filtering. IEEE Transactions on Image Processing, 23(11), 4750-4762.

Pelt, D. M., & Bisseling, R. H. (2014). A medium-grain method for fast 2D bipartitioning of sparse matrices. In Parallel and Distributed Processing Symposium, 2014 IEEE 28th International (pp. 529-539).

2013 Pelt, D. M., Sijbers, J., & Batenburg, K. J. (2013). Fast tomographic reconstruction from highly limited data using artificial neural networks. In 1st International Conference on Tomography of Materials and Structures (ICTMS) (pp. 109-112).

Pelt, D. M., & Batenburg, K. J. (2013). Fast tomographic reconstruction from limited data using artificial neural networks. IEEE Transactions on Image Processing, 22(12), 5238-5251.

2009 Filion, L. et al. (2009). Efficient method for predicting crystal structures at finite temperature: Variable box shape simulations. Physical review letters, 103(18), 188302.