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About

The Brain Image Analysis Unit leverages the latest advancements in machine learning, specifically deep learning, to enhance the processing and analysis of brain imaging data. Our unit specializes in processing diverse brain imaging data sources, including two-photon microscopy, bright-field microscopy images, and MRI scans. We collaborate closely with experts from various scientific disciplines, including computer science, neuroscience, and medicine, to advance the state-of-the-art in brain imaging analysis. As a key player in the Brain/MINDS project, our unit is dedicated to uncovering new insights into the intricacies of primate brain structure through the analysis of high-resolution brain imaging data of the common marmoset monkey.

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
Special Postdoctoral Researcher

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Matthias Schlachter
Special Postdoctoral Researcher

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Mรฉghane Decroocq
JSPS Research Fellow

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

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Michaล‚ Byra
Visiting Scientist
(Assistant Professor, Polish Academy of Sciense, Warsaw)

News

Our Research On Cover Pages

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Our images appeared on a cover of the MIDL (Medical Imaging with Deep Learning) 2021 conference magazine.

Our research inspired a Neuron cover. Artwork by Natsuko Miyazaki (Space-Time Inc.).

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Our image appeared on a cover of the RIKEN RESEARCH magazine.

Alumni and previous guests

  • Jeanne Salle: Summer intern in 2023
  • Binbin Xu : (Associate Professor, MT Mines Ales, Biomedical Data Science) Visiting Scientist 2022
  • Yasutaka Odo : Student trainee and research part-time worker from 2021-2022.
  • Faegheh Yeganli : Research scientist in 2020.

Publications

Our GitHub page: https://github.com/BrainImageAnalysis.

( ๐Ÿ“špublished manuscript, conference paper or short paper, ๐Ÿ“ขabstract, ๐Ÿ“ preprint)

2024

2023

  • ๐Ÿ“ข Deep Learning-Based Multi-Modal Image Processing Using an Interactive Web-Platform: Segmenting and Identifying Neurons in Drosophila
    • M. Schlachter, M. Someya, H. Kazama, H. Skibbe
    • Neuroscience, 2023. (Sendai, Japan)
  • ๐Ÿ“ข An automated pipeline to create a gene expression atlas in the marmoset brain
    • C. Poon, M. F. Rachmadi, M. Byra, M. Schlachter, B. Xu, T. Shimogori, H. Skibbe
    • Neuroscience, 2023. (Sendai, Japan)

2022

  • ๐Ÿ“šWhere Position Matters - Deep learning driven normalization and co-registration of computed tomography in the postoperative analysis of Deep Brain Stimulation
    • M. Reisert, B. Sajonz, P. Reinacher, M. Russe, E. Kellner, H. Skibbe, V. A. Coenen
    • In Neuromodulation: Technology at the Neural Interface, to appear
  • ๐Ÿ“ข 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)
  • ๐Ÿ“ข 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)
  • ๐Ÿ“ข 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. Valdรฉs-Hernรกndez, S. Makin, J.M. Wardlaw, T. Komura, and H. Skibbe
    • Neuroscience, 2022. (Okinawa, Japan)
  • ๐Ÿ“ข 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. Valdรฉs-Hernรกndez, S. Makin, J.M. Wardlaw, T. Komura, and H. Skibbe
    • I nternational Symposium on Artificial Intelligence and Brain Science, 2022. (Okinawa, Japan)
  • ๐Ÿ“ข 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

2020

2019

  • ๐Ÿ“ข 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)
  • ๐Ÿ“ข 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)
  • ๐Ÿ“ข 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)
  • ๐Ÿ“ข 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)
  • ๐Ÿ“ข 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. Liรฉnard, 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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