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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".