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| Hauptseite > Publikationsdatenbank > Towards a Cellular 3D Model of the Human Brain at 1µm Isotropic Resolution |
| Hauptseite > Publikationsdatenbank > Towards a Cellular 3D Model of the Human Brain at 1µm Isotropic Resolution |
| Poster (After Call) | FZJ-2025-04900 |
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2025
Abstract: <b>Background:</b> 3D histological reconstruction is well established at mesoscopic scales (~20µm). At this resolution, the task can be framed as an optimization of spatial transformations driven by pixel-level similarity metrics, typically within coarse-to-fine reconstruction frameworks that integrate both slice-to-slice and histology-to-reference registrations (Amunts et al., 2013; Alkemade et al., 2022).<br><Pushing toward cellular precision (<5µm) introduces several key challenges. First, intensity-based matching becomes unreliable: individual dark features often represent different cells across consecutive sections, so intensities do not correspond one-to-one. Moreover, staining variability and tissue distortions further amplify noise. Second, reference volumes (e.g. MRI or blockface) lack the necessary spatial detail to constrain alignment at the microstructural level. In the absence of a reliable reference volume, sequential reconstruction alone is prone to cumulative alignment drift (commonly known as the “banana effect”), which can artificially straighten curved anatomical structures.<br>We aimed to address these challenges through the development of a novel framework for 3D reconstruction of histological sections that accurately represents the original volume using a three-step feature-matching and global optimization strategy.<br><br><b>Methods:</b> Optical z-scanning enables imaging of twenty depths within each 20 µm thick histological section by gradually shifting the focal plane, resulting in a 1 µm isotropic resolution volume per section. The principal challenge then lies in accurately reconstructing the sections to achieve a faithful representation of the anatomy of the unsliced brain. Our approach involves (i) computing point correspondences between neighbouring sections (e.g. bisected cells or blood vessels). Then, (ii) optimize a global cost function to jointly estimate the spatial coordinates of matched features across all sections, enforcing both matching and smoothness constraints. Finally, (iii) to produce the final volume, polyaffine transformations are fitted to warp each histological section onto the optimized feature positions.<br><br><b>Results:</b> We compared our global reconstruction approach against several other strategies, demonstrating its potential to outperform standard sequential pipelines in terms of alignment accuracy, structural smoothness, and anatomical preservation. The final reconstructed volume maintains natural cortical curvature, resolves laminar organization throughout the cortex (even in oblique cutting planes) and preserves overall brain shape.<br><br><b>Conclusion:</b> Our framework provides a robust, scalable foundation for building cellular-resolution 3D models of the human brain. It makes minimal assumptions about feature types, accommodating both traditional descriptors (e.g., SURF) and ML-based detectors (e.g., LightGlue). The method also generalizes across different tissue stainings and section thicknesses (10–60 µm). Although the approach can operate without a reference volume, one can be incorporated to better preserve overall shape in cases of substantial nonlinear tissue deformation. Future work will generate detailed, region-specific descriptions of cellular organization, which can in turn support realistic simulations of human brain activity.
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