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| Home > Workflow collections > In process > From Learned Representation To Localization: Towards Semantic Integration Of Microscopic Data In The Human Brain |
| Poster (After Call) | FZJ-2026-03286 |
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2026
Abstract: Understanding the organization of the human brain requires analyzing data from different modalities and scales. Such multi-modal analysis is supported by integrating data into reference brain atlases. While microscopic imaging of histological sections provides high-resolution structural information, integration into atlases is challenging, as it typically requires 2D and 3D registration. Alignment becomes particularly challenging for small regions of interest—image patches or small tissue blocks—due to the lack of reliable anatomical landmarks in the limited field of view.We propose localizing histological image patches based on semantic similarity expressed by learned features that encode microstructural patterns. By identifying the most similar patches within a spatially annotated reference frame, we can infer the anatomical position of a query from known reference coordinates. To learn these representations without manual annotations, we adopt a spatial, self-supervised learning strategy in which anatomical proximity serves as a proxy for semantic similarity. The network is trained to produce similar embeddings for neighboring patches and dissimilar ones for distant patches, using spatial distance as the sole supervisory signal.We train and evaluate the network using Nissl-stained microscopic imaging data from human brains. Within a single brain, the learned features capture the anatomical organization—patches from the same region cluster in feature space. However, across brains, inter-subject variability in brain morphology poses a significant challenge. Features learned from reference brains do not reliably transfer to an unseen brain when systematic structural differences are present.To address this, we investigate domain adaptation methods that aim to align feature representations across brains despite morphological variability. By reducing the domain gap, we aim to learn features that are invariant to subject-specific characteristics while preserving the anatomical discriminability needed for accurate patch localization. Preliminary results show that self-supervised learning offers a viable path for integrating sparse microscopic data into reference atlases, and that domain adaptation is a promising approach to achieving robust cross-subject generalization.
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