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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

Henrik Skibbe
Unit Leader

Itsuko Ishii
Technical Staff

Febrian Rachmadi
Special Postdoctoral Researcher

Charissa Poon
Special Postdoctoral Researcher

Matthias Schlachter
Special Postdoctoral Researcher

Méghane Decroocq
JSPS Research Fellow

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

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

News

2024/04
- A MIDL short-paper has been accepted. Congratulations to Charissa! 🎉
- A MIDL paper has been accepted. Congratulations to Meghane! 🎉
- Our paper about instance-level loss functions is online. Congratulations to Febrian! 🎉
2023
- An image generated in our group made it onto the front page of RIKEN Research Winter 2023
- Méghane Decroocq has joined us as a JSPS postdoctoral fellow.
- Our Nature Scientific Reports paper is online! 🎉
- マーモセットの前頭前野結合マップを作成・公開
- Our contribution to the Brain/MINDS Nature Advertisement feature is online
- A MICCAI paper has been accepted. Congratulations to Michal! 🎉
- Our PLoS Biology paper is online! 🎉
- An MIDL paper has been accepted. Congratulations to Febrian! 🎉
- An ISBI paper has been accepted. Congratulations to Charissa! 🎉
2022
- Binbin Xu has joined us as a Visiting Researcher.
- Michal Byra has joined us as a JSPS postdoctoral fellow.
- Our new website is online! 🎉
- Charissa's Kakenhi proposal has been accepted! 🎉😻
- Michal Byra has joined us as a visiting researcher.
- Matthias Schlachter has joined us as a Riken Special Postdoctoral Researcher.
2021
- The Best Paper Award for the 2021 PRIME workshop was awarded to Febrian! 🎉
- Febrian became a Riken Special Postdoctoral Researcher.
- Febrian's Kakenhi proposal has been accepted! 🎉
- Charissa Poon joined us as a postdoctoral researcher.
- Yasutaka Odo has started working with us as a student trainee.
- Our research appeared in the MIDL conference magazine 🎉
- マーモセットの遺伝子発現データベースを公開 (press release)
2020
- Marco Reisert has joined us as a visiting researcher.
- Japan’s big brain project: advances light up marmoset brains (press release)
- Febrian Rachmadi joined us as a research scientist.
- Construction of the Rooms have been completed!! We are moving in.🎉

Our Research On Cover Pages

Our brain 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.).

Our brain connectivity image was used in a cover design of the RIKEN RESEARCH magazine.

One of our images showing brain connectivity was used for the design of a book cover.

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.

( 📚published manuscript, conference paper or short paper, 📢abstract, 📝 preprint)

2024

📚Meta-Learning for Segmention of In Situ Hybriization Gene Expression Images
- C. Poon, M. Byra, T. Shimogori, H. Skibbe
- MIDL, 2024, accepted

📚A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI
- M. F. Rachmadi, M. Byra, H. Skibbe
- In Computers in Biology and Medicine, 2024

📚Multi-scale Stochastic Generation of Labelled Microscopy Images for Neuron Segmentation
- M. Decroocq, B. XU, K. L. Thompson-Peer, A. Moore, H. Skibbe
- MIDL, 2024, accepted

📝Automated Detection of Craniomaxillofacial Fractures From 3D CT Images Using Ensemble Deep Learning-Based Segmentation Models
- M. F. Rachmadi, P. Kreshanti, M. I. Anggraeni,V. Tania, R. E. Yunus, H. Skibbe
- In TechRxiv, 2024

📝Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images
- H. Ju, H. Skibbe, M. Fukui, S. H. Yoshimura, H. Naoki
- In bioRxiv, Cold Spring Harbor Laboratory, 2024

2023

📚Use of DeTerm for Automated Drosophila Dendrite Arbor Terminal Counts
- M. Kimura, J. Tann, O. Wilkes, F. Xu, H. Skibbe, A. W. Moore
- Cold Spring Harbor Protocols, 2023

📚Distinctiveness and continuity in transcriptome and connectivity in the anterior-posterior axis of the paraventricular nucleus of the thalamus
- Y. Shima, H. Skibbe, Y. Sasagawa, N. Fujimori, Y. Iwayama, A. Isomura-Matoba, M. Yano, T. Ichikawa, I. Nikaido, N. Hattori, T. Kato
- In Cell reports, 2023

📚Exploring the performance of implicit neural representations for brain image registration
- M. Byra, C. Poon, M. F. Rachmadi, M. Schlachter, H. Skibbe
- In Scientific reports, Nature Publishing Group, 2023.

📚Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brain
- M. Byra, C. Poon, T. Shimogori, H. Skibbe
- MICCAI, 2023

📚The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain
- H. Skibbe, M.F. Rachmadi, K. Nakae, C. E. Gutierrez, J. Hata, H. Tsukada, C. Poon, K. Doya, P. Majka, M. G. P. Rosa, M. Schlachter, H. Okano, T. Yamamori, S. Ishii, M. Reisert, A. Watakabe.
- In PLoS Biology, 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

📚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

📝Few-shot medical image classification with simple shape and texture text descriptors using vision-language models
- M, Byra, M. F. Rachmadi, H. Skibbe
- In arXiv, 2023

📝PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D Brain Image Registration with Small Patches
- H. Skibbe, M. Byra, A. Watakabe, T. Yamamori, M. Reisert
- In arXiv, 2023

📢 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)

📢 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

📚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

📚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.

📝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.

📝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.

📝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

📚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.

📚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.

📚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 Medical Imaging with Deep Learning 2021, 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.

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)
- Abstract

📢 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)