Henrik Skibbe, Dr. rer. nat.

Research Overview

The Brain Image Analysis Unit develops new algorithms for the processing and analysis of multi-modal brain imaging data such as two-photon, bright-field microscopy images, and MRI.
We are in close collaboration with scientists from the neural-scientific and medical research fields. As a member of the Brain/MINDS project, the unit analyzes image data of the brain of the common marmoset monkey to help better understand the structure and function of the primate brain.

Artificial intelligence plays an important role in contemporary image analysis. In particular, machine learning techniques such as deep learning are indispensable for the automated analysis of large image data-sets. The Brain Image Analysis Unit contributes to this exciting new field by integrating deep learning techniques into new algorithms to improve state-of-the-art processing and analysis of brain imaging data.

Main Research Fields

Informatics

Related Research Fields

Interdisciplinary Science & Engineering / Image Processing / Deep Learning / Pattern Recognition / Life/Health/Medical informatics / High performance computing / Software

Keywords

• Image Analysis
• Deep Learning
• Biomedical Image Processing
• Artificial Neural Networks
• Machine Learning

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

1. H. Skibbe, M. Reisert, K. Nakae, A. Watakabe, J. Hata, H. Mizukami, H. Okano, T. Yamamori, S. Ishii.:
   "PAT - Probabilistic Axon Tracking for Densely Labeled Neurons in Large 3D Micrographs"
   IEEE Transactions on Medical Imaging, Volume: 38 Issue: 1, Jan. 2019, pp 69 - 78
2. *H. Skibbe, M. Reisert.:
   "Spherical Tensor Algebra: A Toolkit for 3D Image Processing"
   Journal of Mathematical Imaging and Vision, July 2017, Volume 58, Issue 3, pp 349 - 381
3. *H. Skibbe, M. Reisert, S. Maeda, M. Koyama, S. Oba, K. Ito, S. Ishii.:
   "Efficient Monte Carlo Image Analysis for the Location of Vascular Entity"
   IEEE Transactions on Medical Imaging, Volume: 34 Issue: 2, Feb. 2015, pp 628 - 643
4. *H. Skibbe, M. Reisert, T. Schmidt, T. Brox, O. Ronneberger, H. Burkhardt.:
   "Fast Rotation Invariant 3D Feature Computation utilizing Efficient Local Neighborhood Operators"
   IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 34, Issue: 8 , Aug. 2012, pp 1563 - 1575
5. *O. Ronneberger, K. Liu, M. Rath, D. Rues, T. Mueller, H. Skibbe, B. Drayer, T. Schmidt, A. Filippi, R. Nitschke, T. Brox, H. Burkhardt, W. Driever.:
   "ViBE-Z: A Framework for 3D Virtual Colocalization Analysis in Zebrafish Larval Brains\"
   Nature Methods volume 9, pages 735 - 742 (2012)
6. *L. Konopleva, K.A. Il'yasov, H. Skibbe, V.G. Kiselev, E. Kellner, B. Dhital, M. Reisert.:
   "Modelfree global tractography"
   NeuroImage, Elsevier, volume 174, pages 576-586, 2018.
7. *H. Skibbe, M. Reisert.:
   "Rotation Covariant Image Processing for Biomedical Applications"
   Computational and Mathematical Methods in Medicine, Special Issue on Mathematical Methods in Biomedical Imaging, Hindawi Publishing Corporation, volume 2013, 2013