Slice to volume registration using neural networks for serial optical coherence tomography of whole mouse brains
Bibliographic record
Abstract
Optical coherence tomography (OCT) has shown an affinity for imaging white matter using intrinsic signals. Combined with an automated vibratome and mosaic imaging, serial blockface histology (SBH) can yield whole-brain white matter images at high resolution. A current drawback of SBH is the lack of real-time information that complicates the localization of brain structures during imaging. To address this, imaged slices can be registered to a pre-existing 3D volume to provide more contextual information during the acquisition. 3D brain image registration is a process where a volume is aligned to a standard template to perform further analysis in a common reference frame. Without a full 3D volume, however, this slice-to-volume registration often proves difficult. The search space is large, and the limited information hampers existing algorithms. In this article, we present a neural network that predicts the 3D position of a 2D slice and aligns it to the corresponding slice in 3D template volume. The network uses a VGG16 backbone to extract features, followed by fully connected layers to predict the transformations. Six mouse brains at a resolution of 25μm, imaged using Serial OCT, have been used to train the network. The loss is calculated by taking the Euclidian distance between the predictions and the ground truth, which has been randomly sampled from the volume. Applications for this model are 2D to 3D slice registration, providing contextual information during serial OCT acquisitions such as the progress, or a parcelization of the current slice into its brain regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".