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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
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)

2023

  • 📝Local and long-distance organization of prefrontal cortex circuits in the marmoset brain
    • A. Watakabe, H. Skibbe, K. Nakae, H. Abe, N. Ichinohe, M. F. Rachmadi, J. Wang, M. Takaji, H. Mizukami, A. Woodward, R. Gong, J. Hata, D. C. Van Essen, H. Okano, S. Ishii, T. Yamamori
    • In Neuron, 2023
  • 📝Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function
    • M.F. Rachmadi, C. Poon, H. Skibbe
    • MIDL, 2023 (to appear)
  • 📝Tomographic tract tracing and data driven approaches to unravel complex 3D fiber anatomy of DBS relevant prefrontal projections to the diencephalic-mesencephalic junction in the marmoset
    • V. A. Coenen, A. Watakabe, H. Skibbe, T. Yamamori, M. D. Döbrössy, B. E.A. Sajonz, P. C. Reinacher, M. Reisert
    • In Brain Stimulation, 2023
  • 📝An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain
    • C. Poon, M.F. Rachmadi, M. Byra, M. Schlachter, B. Xu, T. Shimogori, H. Skibbe
    • In IEEE International Symposium on Biomedical Imaging, to appear.
  • 📝A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging
    • C. Poon, Teikari P., M.F. Rachmadi, H. Skibbe, K. Hynynen
    • In Scientific Data, 2023

2022

  • 📝Improving the prediction of white matter hyperintensities evolution in brain MRI of patients with small vessel disease using stroke lesions information
    • M.F. Rachmadi, M.d.C. Valdés-Hernández, S. Makin, J. Wardlaw, H. Skibbe
    • In bioRxiv, Cold Spring Harbor Laboratory, 2022.
  • 📝Where 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
  • 📝Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range
    • J. Hata, K. Nakae, H. Tsukada, A. Woodward, Y. Haga, M. Iida, A. Uematsu, F. Seki, N. Ichinohe, R. Gong, T. Kaneko, D. Yoshimaru, A. Watakabe, H. Abe, T. Tani, H. Skibbe, M. Maeda, F. Papazian, K. Hagiya, N. Kishi, T. Shimogori, T. Yamamori, H. James Okano, H. Okano
    • In bioRxiv, Cold Spring Harbor Laboratory, 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)

top.txt · Last modified: 2023/05/29 14:02 by marhenrikoset