Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks
Bibliographic record
Abstract
Abstract Background and Objective A variety of genetic mutations are known to affect cell proliferation and apoptosis during organism development, leading to structural birth defects such as facial clefting. Yet, the mechanisms how these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied in high detail using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points. However, the large number of LSM images captured at cellular resolution precludes manual analysis. Thus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way. Methods We developed, trained, and evaluated convolutional neural networks (CNNs) for segmenting tissues, cells, and specifically proliferating cells in LSM datasets. We compared the automatically extracted tissue and cell annotations to corresponding manual segmentations for three specific applications: (i) tissue segmentation (neural ectoderm and mesenchyme) in nuclear-stained LSM images, (ii) cell segmentation in nuclear-stained LSM images, and (iii) segmentation of proliferating cells in Phospho-Histone H3 (PHH3)-stained LSM images. Results The automatic CNN-based tissue segmentation method achieved a macro-average F-score of 0.84 compared to a macro-average F-score of 0.89 comparing corresponding manual segmentations from two observers. The automatic cell segmentation method in nuclear-stained LSM images achieved an F-score of 0.57, while comparing the manual segmentations resulted in an F-score of 0.39. Finally, the automatic segmentation method of proliferating cells in the PHH3-stained LSM datasets achieved an F-score of 0.56 for the automated method, while comparing the manual segmentations resulted in an F-score of 0.45. Conclusions The proposed automatic CNN-based framework for tissue and cell segmentation leads to results comparable to the inter-observer agreement, accelerating the LSM image analysis. The trained CNN models can also be applied for shape or morphological analysis of embryos, and more generally in other areas of cell biology.
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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 | 0.000 |
| 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".