Reconstruction of missing cells by a Killing energy minimizing nonrigid image registration
Bibliographic record
Abstract
Fluorescent microscopy has been a popular and important tool for studying live cells. One challenge of analyzing cell images obtained from fluorescent microscopy is that cells in fluorescent images frequently disappear and reappear, making cell tracking difficult. In this paper, we present an image registration approach which can reconstruct both the cell appearance and location of the missing cells from the image frames where the cells become invisible. The idea is to perform an image registration on the images before and after a cell disappears. The missing image frames between these two images are given by the intermediate registration results. The formulation is based on the nonrigid particle registration model, which captures soft deformation of the cells. In addition, to obtain natural and more rigid cell movements such as translation and rotation, we propose a new registration technique which is Killing energy minimizing, motivated by the fact that a Killing vector field with zero Killing energy will generate an isometric deformation. We will present reconstruction results of C2C12 cells in fluorescent images to illustrate the effectiveness of our model by different numerical examples.
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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".