Application of neural networks to the segmentation of microscopy images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An investigation of intelligent image processing algorithms to segment chromosomes in three-dimensional (3D) microscopy images taken by a confocal light microscope is presented. The use of this confocal light microscope allows biologists to observe live (or preserved) dividing cells in 3D. However, the top and bottom surfaces of these image features are indistinct, therefore requiring feature enhancement and segmentation of the chromosomes. In the proposed approach, a model-based neural network is first used to improve the quality of the images, and then the newly proposed self-organizing tree map (SOTM) is applied to perform segmentation. Segmentation algorithms are developed to work both on 2D dataset, based on a projection of the three-dimensional dataset, and on 3D dataset directly. The 3D approach to segmenting individual chromosome features preserves the 3D orientations in relation to the surrounding cell volume. The proposed algorithms perform very satisfactorily in the 3D case. Examples are provided to demonstrate the performance of the proposed algorithms.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it