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| Hauptseite > Publikationsdatenbank > The status quo of automated cytoarchitecture analysis: Where are we, and where are we going? > print |
| Hauptseite > Publikationsdatenbank > The status quo of automated cytoarchitecture analysis: Where are we, and where are we going? > print |
| 001 | 1018410 | ||
| 005 | 20231123201916.0 | ||
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| 100 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 7th BigBrain Workshop |c Reykjavík |d 2023-10-04 - 2023-10-06 |w Iceland |
| 245 | _ | _ | |a The status quo of automated cytoarchitecture analysis: Where are we, and where are we going? |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a Cytoarchitectonic brain maps provide a microstructural reference for multi-modal human brain atlases, representing important indicators for brain connectivity and function. Cytoarchitectonic areas are defined by characteristic microstructural cell distributions, including the size, shape, type, orientation, and density of neurons, as well as their distinct laminar and columnar arrangement. High-resolution microscopic scans of histological human brain sections enable identifying cytoarchitectonic brain areas. Modern high-throughput microscopic scanners enable large-scale image acquisition, resulting in petabyte-scale microscopic imaging datasets that provide the foundation for next-generation brain atlases. As established cytoarchitectonic brain mapping methods based on statistical image analysis do not scale to such large datasets, ongoing research aims to develop methods for automatic classification and characterization of cytoarchitecture based on large amounts of high-resolution images.In this presentation, we will give an overview of the current state of automated cytoarchitecture analysis and provide an outlook on future developments in the field. We will discuss the roles, potentials, and challenges of supervised learning, self-supervised representation learning, and graph-based inference at whole-brain level in the context of cytoarchitecture analysis. Finally, we will comment on the potential impact of novel methods and technologies on the field, including zero-shot learning, data-driven cytoarchitectonic mapping, multi-modal latent space fusion, and exascale computing. |
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