Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
Bibliographic record
Abstract
Accurate, label-free imaging of intestinal organoids is crucial for studying their morphology, growth dynamics, and responses to environmental stimuli. Holotomography (HT) provides high-resolution, three-dimensional (3D) visualization of live organoids without the need for fluorescent markers, thereby minimizing phototoxicity and preserving sample integrity. Real-time phase-based imaging allows continuous, label-free tracking of structural and functional changes. By using the refractive index as an intrinsic imaging contrast, this method enables quantification of biophysical properties such as volume, protein density, and protein content. The imaging data are further processed through machine learning-driven segmentation and feature extraction to support consistent, high-throughput analysis. This protocol details the complete experimental workflow for employing low-coherence HT in organoid research, covering organoid preparation, imaging acquisition, and machine learning-based data analysis. By integrating computational segmentation and quantitative assessments, this approach enables unbiased evaluation of key organoid properties, including viability, structural organization, and drug response. The ability to capture real-time morphological changes at subcellular resolution makes this protocol highly applicable to organoid-based studies in regenerative medicine, disease modeling, and pharmaceutical screening. The step-by-step methodology outlined here facilitates reproducibility and broad adaptation across different organoid systems.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".