Stochastic Modeling of Normal and Tumor Tissue Microstructure for High-Frequency Ultrasound Imaging Simulations
Bibliographic record
Abstract
High-frequency (20-60 MHz) ultrasound images of preclinical tumor models are sensitive to changes in tissue microstructure that accompany tumor progression and treatment responses, but the relationships between tumor microanatomy and high-frequency ultrasound backscattering are incompletely understood. This paper introduces a 3-D microanatomical model in which tissue is treated as a population of stochastically positioned spherical cells consisting of a spherical nucleus surrounded by homogeneous cytoplasm. The model is used to represent the microstructure of both healthy mouse liver and an experimental liver metastasis that are analyzed using 4 ',6-diamidino-2-phenylindole- and hematoxylin and eosin-stained histology specimens digitized at 20 x magnification. The spatial organization of cells is controlled in the model by a Gibbs-Markov point process whose parameters are tuned to maximize the similarity of experimental and simulated tissue microstructure, which is characterized using three descriptors of nuclear spatial arrangement adopted from materials science. The model can accurately reproduce the microstructure of the relatively homogeneous healthy liver and the average cell clustering observed in the experimental metastasis, but is less effective at reproducing the spatial heterogeneity of the experimental metastasis. The model provides a framework for computational investigations of the effects of individual microstructural and acoustic properties on high-frequency backscattering.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".