Schiffer team

LARGE-SCALE ARTIFICIAL INTELLIGENCE FOR BRAIN MAPPING

UNCOVERING MICROSTRUCTURAL BRAIN ORGANIZATION WITH THE HELP OF AI

Facing the challenges of brain complexity and big data analytics, research at INM-1 addresses human brain organization on its different scales, hand in hand with brain simulation and the investigation of learning in artificial and biological neural networks to advance our understanding of cognition, which opens perspectives to discover novel mechanisms for AI.

The research group is embedded into the Big Data Analytics group headed by Timo Dickscheid, which develops methods for high throughput analysis and 3D reconstruction of microscopic image data, and neuroinformatics solutions for high-resolution brain atlases. In this context, our Helmholtz AI young investigator group focuses on the development of artificial intelligence methods for large-scale microstructural mapping of the human brain. Our work aims at data-driven holistic characterization of human brain architecture at the microstructural scale, based on petabyte-scale microscopic datasets reflecting cyto-, fiber-, and chemoarchitecture as complementary organizational principles. Driven by continuously increasing image resolutions and data volumes, our methods are designed for large-scale distributed operation on High-Performance Computing (HPC) systems.

Team

Alexander Oberstraß, PhD student

Jan-Oliver Kropp, PhD student

Zeynep Boztoprak, Master student

Torben Glass, Master student

Juan Ignacio Farina, Master student

Publications and projects

Ma, Jun, Ronald Xie, Shamini Ayyadhury, Cheng Ge, Anubha Gupta, Ritu Gupta, Song Gu, et al. 2024. “The Multimodality Cell Segmentation Challenge: Toward Universal Solutions.” Nature Methods 21 (6): 1103–13. https://doi.org/10.1038/s41592-024-02233-6.

Nebli, Ahmed, Christian Schiffer, Meiqi Niu, Nicola Palomero-Gallagher, Katrin Amunts, and Timo Dickscheid. 2024. “Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain.” Neuroinformatics, July. https://doi.org/10.1007/s12021-024-09673-7.

Oberstrass, Alexander, Jordan DeKraker, Nicola Palomero-Gallagher, Sascha E. A. Muenzing, Alan C. Evans, Markus Axer, Katrin Amunts, and Timo Dickscheid. 2024. “Analyzing Regional Organization of The Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding.” In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI56570.2024.10635467.

Kropp, Jan-Oliver, Christian Schiffer, Katrin Amunts, and Timo Dickscheid. 2024. “Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in The Human Brain.” In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI56570.2024.10635384.

Schiffer C, Spitzer H, Kiwitz K, Unger N, Wagstyl K, Evans AC, et al. Convolutional neural networks for cytoarchitectonic brain mapping at large scale. Neuroimage. 2021; 240(118327):118327. DOI: 10.1016/j.neuroimage.2021.118327

Schiffer C, Harmeling S, Amunts K, Dickscheid T. 2D histology meets 3D topology: Cytoarchitectonic brain mapping with graph neural networks. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.2021. p. 395–404. DOI: 10.1007/978-3-030-87237-3_38

C. Schiffer, L. Schuhmacher, K. Amunts, and T. Dickscheid, “Learning to predict cutting angles from histological human brain sections,” in Medical Imaging with Deep Learning (MIDL 2021), 2021. [Online]. Available: https://openreview.net/forum?id=9CSM4yQmZiN

C. Schiffer, K. Amunts, S. Harmeling, and T. Dickscheid, “Contrastive Representation Learning For Whole Brain Cytoarchitectonic Mapping In Histological Human Brain Sections,” in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI 2021), 2021, pp. 603–606. doi: 10.1109/ISBI48211.2021.9433986.

K. Kiwitz, C. Schiffer, H. Spitzer, T. Dickscheid, and K. Amunts, “Deep learning networks reflect cytoarchitectonic features used in brain mapping,” Scientific Reports, vol. 10, Art. no. 1, 2020, doi: 10.1038/s41598-020-78638-y.

Schiffer team

UNCOVERING MICROSTRUCTURAL BRAIN ORGANIZATION WITH THE HELP OF AI

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Schiffer team

UNCOVERING MICROSTRUCTURAL BRAIN ORGANIZATION WITH THE HELP OF AI

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