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About

The Brain Image Analysis Unit integrates machine learning techniques such as deep learning into new algorithms to improve state-of-the-art processing and analysis of brain imaging data. Our unit processes 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.

Members

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Henrik Skibbe
Unit Leader

Itsuko Ishii
Technical Staff

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Febrian Rachmadi
Special Postdoctoral Researcher

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Charissa Poon
Postdoctoral Researcher

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Matthias Schlachter
Special Postdoctoral Researcher

NEW: Postdoc
Postdoctoral Researcher

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Marco Reisert
Visiting Scientist
(Group Leader, University Medical Center Freiburg, Germany)

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Michał Byra
JSPS Postdoctoral Fellow
(Assistant Professor, Polish Academy of Sciense, Warsaw)

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Binbin Xu
Visiting Scientist
(Associate Professor, MT Mines Ales, Biomedical Data Science)

News

Alumni and previous guests

  • Yasutaka Odo : Student trainee and research part-time worker from 2021-2022.
  • Faegheh Yeganli : Research scientist in 2020.

Selected publications

( 📝:manuscript, paper or short paper, 📢:abstract)

2022

  • 📝The Brain/MINDS Marmoset Connectivity Atlas: Exploring bidirectional tracing and tractography in the same stereotaxic space
    • H. Skibbe, M.F. Rachmadi, K. Nakae, C. E. Gutierrez, J. Hata, H. Tsukada, C. Poon, K. Doya, P. Majka, M. G. P. Rosa, H. Okano, T. Yamamori, S. Ishii, M. Reisert, A. Watakabe.
    • In bioRxiv, Cold Spring Harbor Laboratory, 2022.
  • 📝A spiking neural network builder for systematic data-to-model workflow
    • C. E. Gutierres, H. Skibbe, H. Musset, K. Doya.
    • Frontiers in Neuroinformatics (to appear), 2022.
  • 📝Distinctiveness and continuity in transcriptome and connectivity in the anterior-posterior axis of the paraventricular nucleus of thalamus
    • Shima, Y., Skibbe, H, Sasagawa, Y., Fujimori, N., Nikaido, I., Hattori, N. and Kato, T.
    • In bioRxiv, Cold Spring Harbor Laboratory, 2022.
  • 📢 Semi-supervised semantic segmentation of in situ hybridization gene expression in the marmoset brain
    • C. Poon, M.F. Rachmadi, M. Byra, T. Shimogori, and H. Skibbe
    • Society for Neuroscience, 2022. (California, USA)
  • 📢 Semi-supervised contrastive learning for semantic segmentation of in situ hybridization gene expression in the marmoset brain
    • C. Poon, M.F. Rachmadi, M. Byra, T. Shimogori, and H. Skibbe
    • International Symposium on Artificial Intelligence and Brain Science, 2022. (Okinawa, Japan)
  • 📢 Development of a Data-driven Prediction Model for the Evolution of White Matter Hyperintensities using Deep Learning: Progress and Challenges
    • M.F. Rachmadi, M.d.C. Valdés-Hernández, S. Makin, J.M. Wardlaw, T. Komura, and H. Skibbe
    • Neuroscience, 2022. (Okinawa, Japan)
  • 📢 Development of a Data-driven Prediction Model for the Evolution of White Matter Hyperintensities using Deep Learning: Progress and Challenges
    • M.F. Rachmadi, M.d.C. Valdés-Hernández, S. Makin, J.M. Wardlaw, T. Komura, and H. Skibbe
    • I nternational Symposium on Artificial Intelligence and Brain Science, 2022. (Okinawa, Japan)
  • 📢 Semi-supervised contrastive learning for semantic segmentation of in situ hybridization gene expression in the marmoset brain
    • C. Poon, M.F. Rachmadi, M. Byra, T. Shimogori, and H. Skibbe
    • Neuroscience, 2022. (Okinawa, Japan)

2021

  • 📝Connectional architecture of the prefrontal cortex in the marmoset brain
    • Watakabe, A., Skibbe, H. Nakae, K., Abe, H., Ichinohe, N., Wang, J., Takaji, M., Mizukami, H., Woodward, A., Gong, R., Hata, J., Okano, H., Ishii, S. and Yamamori, T.
    • In bioRxiv, Cold Spring Harbor Laboratory, 2021.
  • 📝Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation-Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution
    • Rachmadi, M. F., Valdés-Hernández, M. del C, Maulana, R., Wardlaw, J., Makin, S. and Skibbe, H.
    • In International Workshop on PRedictive Intelligence In MEdicine, 2021.
  • 📝Semi-supervised Image-to-Image translation for robust image registration
    • Skibbe, H., Watakabe, A., Rachmadi, F., Gutierrez, C.E, Nakae,K. and Yamamori, T.
    • In Proc. of the Medical Imaging with Deep Learning 2021, 2021.
  • 📝Cellular-resolution gene expression profiling in the neonatal marmoset brain reveals dynamic species-and region-specific differences
    • Kita, Y., Nishibe, H., Wang, Y., Hashikawa, T., Kikuchi, S. S., Mami, U,. Yoshida, A. C., Yoshida, C., Kawase, T., Ishii, S., Skibbe, H. and Shimogori, T.
    • In Proceedings of the National Academy of Sciences, National Acad Sciences, volume 118, 2021.

2020

  • 📝Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
    • Gutierrez, Carlos Enrique, Skibbe, Henrik, Nakae, Ken, Tsukada, Hiromichi, Lienard, Jean, Watakabe, Akiya, Hata, Junichi, Reisert, Marco, Woodward, Alexander, Yamaguchi, Yoko, Yamamori, Tetsuo, Okano, Hideyuki, Ishii,Shin and Doya, Kenji
    • In Scientific reports, Nature Publishing Group, volume 10, 2020.

2019

  • 📢 Mapping connectivity of the marmoset prefrontal cortex
    • A. Watakabe, H. Skibbe, K. Nakae, J. Wang, M. Takaji, H. Mizukami, A. Woodward, R. Gong, Y. Yamaguchi, J. Hata, H. Okano, S. Ishii and T. Yamamori
    • Neuroscience, 2019. (Niigata, Japan)
  • 📢 Fully automated data processing for mapping connectivity of the marmoset prefrontal cortex
    • H. Skibbe, A. Watakabe, K. Nakae, C. E. Gutierrez, A. Woodward, H. Tsukada, R. Gong, J. Hata, K. Doya, H. Okano, T. Yamamori and S. Ishii
    • Neuroscience, 2019. (Niigata, Japan)
  • 📢 A fully automated, AI-driven pipeline for the determination of the marmoset brain connectivity based on tracer data obtained with the TissueCyte microscope
    • H Skibbe, A. Watakabe, K. Nakae, C. E. Gutierrez, A. Woodward, H. Tsukada, R. Gong, J. Hata, H. Okano, T. Yamamori and S. Ishii
    • International Symposium of Brain/MINDS ISBM, 2019. (Tokyo, Japan)
  • 📢 Macro-scale connectome by diffusion MRI of exvivo marmoset brain with a pipeline of global fiber reconstruction
    • K. Nakae, J. Hata, H. Skibbe, A. Woodward, C. E. Gutierrez, H. Tsukada, G. Rui, H. Okano and S. Ishii
    • International Symposium of Brain/MINDS ISBM, 2019. (Tokyo, Japan)
  • 📢 Multi-objective Parameter Optimization of DWI-based Global Fiber Tracking with Neuronal Tracer Signal as a Reference
    • C. Enrique Gutierrez, H. Skibbe, K. Nakae, J. Liénard, A. Woodward, A. Watakabe, H. Tsukada, J. Hata, H. Okano, T. Yamamori, Y. Yamaguchi, S. Ishii and K. Doya
    • International Symposium of Brain/MINDS ISBM, 2019. (Tokyo, Japan)

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