Biomechanics‐Driven 3D Architecture Inference from Histology Using CellSqueeze3D
Bibliographic record
Abstract
Conventional 2D analysis of hematoxylin and eosin (H&E)-stained images is fundamentally limited by the tissue thickness, as cellular overlap and morphological changes in the compressed perspective obscure distinct cell boundaries. To address this, it develops CellSqueeze3D, a computational framework that reconstructs the 3D spatial distribution and size of individual cells from a single H&E-stained section. Founded on the principle that 2D cell compression preserves 3D geometry, the method employs a hybrid Particle Swarm Optimization (PSO) approach with biomechanical constraints to infer biologically plausible reconstructions. Validation shows that the nuclear-to-cytoplasmic (N/C) ratio distribution derived from the predicted cell radii differs significantly from random assignments (p = 1.39e-80). By employing projected cell boundaries, the 3D-informed cellular classifier surpassed traditional methods (AUC increases of 0.136 and 0.069). The resulting morphological metrics also revealed strong associations with key gene expression patterns, providing prognostic insights. Furthermore, cellular and nuclear size indices from CellSqueeze3D significantly predict the mutation status of 21 genes in TCGA cohorts, achieving a median AUROC above 0.65 in fivefold cross-validation. This study demonstrates that fully utilizing the previously untapped 3D spatial information from a single slice significantly enhances computational pathology and quantitative tissue phenotyping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".