A framework for computing high resolution 3D microscopic volumes
Bibliographic record
Abstract
Introduction: Whole-slide imaging microscopy is more available recently but 3D analysis of such histological images still remains a bottleneck. For generating 3D microscopic volumes of tissue blocks, histology images need to be spatially aligned, which is problematic due to inevitable global and local deformations introduced during processing, especially in free-floating sections. We propose a framework to create high-resolution 3D microscopic volume datasets by correcting and combining micro-scale images. Methods and Results: Due to the substantial local deformation of free-floating sections, the core method in our proposed framework is the shape alignment between histology and corresponding blockface image. Matching pair-points are manually selected in both images, especially in locations with high confidence and around the border of the tissue. Based on these pair-points, a sparse deformation map is generated. To evenly distribute deformations around the tissue, the map is refined using a thin plate spline interpolation. We tested the method on a series of human subcortical slices sectioned at a thickness of 50um. Stained slides were digitized using the Axioscan 7 multiplex scanner at an in-plane resolution of 0.3um/pixel and downsampled to the resolution of blockface image. The result of one of these registered histology images is show in figure 1. To improve the alignment between the histology images, a feature matching approach will be utilized in combination with a resolution upsampling method and a continuous 3D volume will be generated using interpolation between images. Conclusions: In this work we have discussed a semi-manual technique to reconstruct 3D histology volumes, with special focus on correcting free floating stained histology deformations using the blockface images. The high-resolution 3D microscopic volume provides advanced analytical opportunities, such as co-registering with other volumetric images (e.g. MRI), or perform spatial 3D analysis. In future work, we plan to improve the procedure by making the point selection semi- or fully automatic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".