Dr. ir. Hakim Achterberg

Hakim Achterberg

Office: Na 25-02
Phone: +31-10-7044187
Email: h.achterberg@erasmusmc.nl
Website: https://www.linkedin.com/in/hakimachterberg

Curriculum Vitae

Hakim Achterberg was born on 1984 in Tilburg, The Netherlands. He finished his secondary education at the Theresia Lyceum in 2003. Following high school, Hakim enrolled in the Bachelor’s program in Biomedical Technology at the Eindhoven University of Technology. During this program Hakim first came in contact with Image Analysis. Having completed his Bachelor’s degree, Hakim moved on to the Master’s program in Biomedical Engineering and started specializing in Biomedical Image Analysis. During his Master’s education, Hakim spent an exchange year at Umeå University in Sweden, where he strengthened his knowledge of computing science, image analysis and visualization further. Once back in the Netherlands Hakim performed an internships at the x-ray predevelopment group of Philips Healthcare in Best and the Image Processing and Analysis Group at Yale University led by James Duncan. In 2009 he finished his thesis “Optimal Acquisition Schemes in High Angular Diffusion Imaging” and received his M.Sc. degree from the Eindhoven University of Technology

In December 2009 Hakim started his PhD project in the BIGR group at the Erasmus Medical Centre Rotterdam. His work focused on shape analysis of brain structures in relation to neuro-degenerative diseases. He was supervised by W. Niessen (promotor) and M. de Bruijne (co-promotor). His work focused on two main points: first the analysis of the imaging data from the Rotterdam Scan Study, a large cohort study on aging, in which imaging biomarkers are related to the develop of dementia, and second there is the evaluation and improvement of methods for shape analysis. During his PhD Hakim also was involved of the creation of the MICCAI student board, a student body that organizes activities around the MICCAI conference, of which he was the president in 2012 and 2013.

As of December 2013, Hakim is working as a research software engineer in the BIGR group, where he focusses on the IT infrastructure for large imaging studies. This includes creating software for the management, storage, transfer, processing and annotation of large imaging dataset. For the processing both automatic pipelines as well as integrated annotation tools are used. Hakim is the main developer for a number of tools, including the open-source workflow engine Fastr and python XNAT communication package xnatpy.

Research lines

Research IT Infrastructure Development

Neuro Image Analysis

Model-based Medical Image Analysis


Google Scholar profile

Journal Articles

  1. Achterberg, H. C. (2017). Quantitative Imaging Biomarkers for Hippocampus-based Dementia Prediction.
  2. Achterberg, H. C., Koek, M., & Niessen, W. J. (2016). Fastr: a workflow engine for advanced data flows in medical image analysis. Frontiers in ICT, 3, 15.
  3. Achterberg, H. C., van der Lijn, F., den Heijer, T., Vernooij, M. W., Ikram, M. A., Niessen, W. J., & de Bruijne, M. (2014). Hippocampal shape is predictive for the development of dementia in a normal, elderly population. Human Brain Mapping, 35(5), 2359–2371.
  4. Bron, E. E., Steketee, R. M. E., Houston, G. C., Oliver, R. A., Achterberg, H. C., Loog, M., … others. (2014). Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia. Human Brain Mapping, 35(9), 4916–4931.
  5. Prčkovska, V., Achterberg, H. C., Bastiani, M., Pullens, P., Balmashnova, E., ter Haar Romeny, B. M., … Roebroeck, A. (2013). Optimal short-time acquisition schemes in high angular resolution diffusion-weighted imaging. International Journal of Biomedical Imaging, 2013.
  6. Plaisier, A., Achterberg, H., de Groot, M., Leemans, A., Reiss, I. K. M., Niessen, W. J., … Klein, S. Standardized Workflow for Constructing a Site-Specific DTI Template of the Preterm Brain. MR Imaging of the Preterm Brain, 107.

Conference Articles

  1. Koek, M., Achterberg, H., de Groot, M., Vast, E., Klein, S., & Niessen, W. (2015). Population Imaging Study IT Infrastructure: An Automated Continuous Workflow Approach. In 1 st MICCAI Workshop on (p. 31).
  2. Achterberg, H., Koek, M., & Niessen, W. (2015). Fastr: a workflow engine for advanced data flows. In 1st MICCAI Workshop on Management and Processing of Images for Population Imaging (Munich) (pp. 39–46).
  3. van Opbroek, A., Achterberg, H. C., & de Bruijne, M. (2015). Feature-space transformation improves supervised segmentation across scanners. In Medical Learning Meets Medical Imaging (pp. 85–93). Springer, Cham.
  4. Achterberg, H. C., Poot, D. H. J., van der Lijn, F., Vernooij, M. W., Ikram, M. A., Niessen, W. J., & de Bruijne, M. (2013). Local appearance features for robust MRI brain structure segmentation across scanning protocols. In Medical Imaging: Image Processing (p. 866905).
  5. Niessen, W., Vrooman, H., de Boer, R., van der Lijn, F., Achterberg, H., Koek, M., … others. (2012). Quantitative imaging biomarkers in neurologic disease: Population study perspective. In Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on (pp. 911–911). IEEE.
  6. Achterberg, H., de Bruijne, M., van der Lijn, F., den Heijer, T., Vernooij, M., Ikram, M. A., & Niessen, W. (2012). Hippocampal shape predicts development of dementia in the general population. In 97th Annual Meeting of the Radiological Society of North America.
  7. Terzopoulos, V., Achterberg, H. C., Plaisier, A., Heemskerk, A. M., de Groot, M., Poot, D., … Klein, S. (2012). A 3D atlas of MR diffusion parameters in the neonatal brain. In Medical Image Computing and Computer-Assisted Intervention.
  8. Vrooman, H. A., Jasperse, M. M. S., Koek, M., van der Lijn, F., Achterberg, H. C., de Bruijne, M., … Niessen, W. J. (2011). A computer-aided diagnosis system for the early and differential diagnosis of neurodegenerative disease. In European Congress of Radiology.
  9. Achterberg, H. C., Van Der Lijn, F., Den Heijer, T., Van Der Lugt, A., Breteler, M. M. B., Niessen, W. J., & De Bruijne, M. (2010). Prediction of Dementia by Hippocampal Shape Analysis. In MLMI (pp. 42–49).