Description
co-Chairs: Claire Walsh, Jussi Tohka
The characterization of cytoarchitecture in the human brain provides an essential building block for the creation of a high-resolution multi-modal brain atlas. Cytoarchitecture is defined by the spatial organization of neuronal cells, including their shape, density, size, cell type, as well as their columnar and laminar arrangement, which differ between brain regions. High-throughput...
Introduction. Extracting quantitative information from the whole brain in histology is one big challenge in neuroscience [1]. Software programs [2] helped in histological quantification, although being time-consuming and relying on individual expertise. Further, machine learning allowed the automation of processes such as segmentation [3] and classification [4], but being, at best,...
Advances in microscopic imaging and high-performance computation have made it possible to analyze the complex cellular structure of the human brain in great detail. This progress has greatly aided in brain mapping and cell segmentation, leading to the development methods for automated analysis of tissue architecture and cell distribution in histological brain sections. However, histological...
Human brain atlases play a crucial role in providing a spatial framework to organize information derived from diverse research on brains, integrating multimodal and multiresolution images. The preprocessing stage is crucial for enhancing the quality of the images, addressing issues related to degradation, and ensuring that subsequent analyses and interpretations are based on reliable and...
Quantifiable and interpretable descriptors of nerve fiber architecture at microscopic resolution are an important basis for a deeper understanding of human brain architecture. 3D polarized light imaging (3D-PLI) provides detailed insights into the course and geometry of nerve fibers in whole postmortem brain sections, represented in large datasets. The large amounts of data, combined with...
Precise instance segmentation is a critical part of many fields of research in biomedical imaging. One key challenge is applying models to new data domains, typically involving pre-training a model on a larger corpus of data and fine-tuning it with new annotations for each specific domain. This process is labor- intensive and requires creating and maintaining multiple branched versions of the...
