SegDecon bridges histology and transcriptomics through AI-based nuclei segmentation and image-informed spatial deconvolution
Bibliographic record
Abstract
Precise spatial mapping of cellular composition is a central goal in spatial transcriptomics (ST), yet current methods often assume uniform or manually estimated cell counts across spatial spots, potentially distorting biological interpretation. Here, we present SegDecon, a computational framework that integrates image-derived cell count estimation into Bayesian deconvolution. SegDecon enhances nuclei segmentation using Hue-Saturation-Value (HSV) color space transformation, morphological filtering, and deep learning-based instance segmentation. It quantifies nuclei per spatial spot and refines cell-type deconvolution through tailored Gamma priors in a modified cell2location model. Evaluated on high-resolution mouse brain ST data, SegDecon demonstrates improved correlation with ground truth, particularly in resolving low-abundance and spatially restricted cell types. This approach provides a reproducible and accessible solution to bridge histology with transcriptomic deconvolution, improving both resolution and biological fidelity. Source code is available at: https://github.com/CiiM-Bioinformatics-group/SegDecon.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".