A Review of Intrinsic Optical Imaging Serial Blockface Histology (ICI-SBH) for Whole Rodent Brain Imaging
Bibliographic record
Abstract
In recent years, multiple serial histology techniques were developed to enable whole rodent brain imaging in 3-D. The main driving forces behind the emergence of these imaging techniques were the genome-wide atlas of gene expression in the mouse brain, the pursuit of the mouse brain connectome, and the BigBrain project. These projects rely on the use of optical imaging to target neuronal structures with histological stains or fluorescent dyes that are either expressed by transgenic mice or injected at specific locations in the brain. Efforts to adapt the serial histology acquisition scheme to use intrinsic contrast imaging (ICI) were also put forward, thus leveraging the natural contrast of neuronal tissue. This review focuses on these efforts. First, the origin of optical contrast in brain tissue is discussed with emphasis on the various imaging modalities exploiting these contrast mechanisms. Serial blockface histology (SBH) systems using ICI modalities are then reported, followed by a review of some of their applications. These include validation studies and the creation of multimodal brain atlases at a micrometer resolution. The paper concludes with a perspective of future developments, calling for a consolidation of the SBH research and development efforts around the world. The goal would be to offer the neuroscience community a single standardized open-source SBH solution, including optical design, acquisition automation, reconstruction algorithms, and analysis pipelines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".