• Our GitHub page: https://github.com/BrainImageAnalysis.
• The BMCR Resources moved to a new place

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

• 📝Serial Two-Photon Tomography Imaging of the whole marmoset brain for neuroanatomical analyses
  • A. Watakabe, T, Tani, H. Abe, H. Skibbe, N. Ichinohe, T. Yamamori
  • In bioRxiv, 2024

• 📚Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images
  • H. Ju, H. Skibbe, M. Fukui, S. H. Yoshimura, H. Naoki
  • iScience, 2024

• 📚Meta-Learning for Segmention of In Situ Hybridization 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

• 📢 A 3D gene expression atlas of the adult marmoset brain
  • C. Poon, M. Byra, M. F. Rachmadi, M. Schlachter, M. Decroocq, B. Xu, B. Fulcher, T. Shimogori, H. Skibbe
  • Neuroscience, 2024, (Fukuoka, Japan)

• 📢 Marmoset whole brain network model based on the structural connectome and gene expression map reproduces the functional connectome with balanced excitation and inhibition
  • H. Tsukada, K. Nakae, J. Hata, H. T. Hamada, K. Tokuda, C. E. Gutierrez, H. Skibbe, C. Poon, A. Woodward, S. Ishii, T. Shimogori, H. Okano, K. Doya
  • Neuroscience, 2024, (Fukuoka, Japan)

• 📚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

• 📚Study of Dendrite Differentiation Using Drosophila Dendritic Arborization Neurons
  • Tann JY, Xu F, Kimura M, Wilkes OR, Yoong LF, Skibbe H, Moore AW.
  • 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)

• 📚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)

• 📚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.

• 📚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.

• 📢 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)