Automatic anatomical labeling of the complete cerebral vasculature in mouse models
Bibliographic record
Abstract
Study of cerebral vascular structure broadens our understanding of underlying variations, such as pathologies that can lead to cerebrovascular disorders. The development of high resolution 3D imaging modalities has provided us with the raw material to study the blood vessels in small animals such as mice. However, the high complexity and 3D nature of the cerebral vasculature make comparison and analysis of the vessels difficult, time-consuming and laborious. Here we present a framework for automated segmentation and recognition of the cerebral vessels in high resolution 3D images that addresses this need. The vasculature is segmented by following vessel center lines starting from automatically generated seeds and the vascular structure is represented as a graph. Each vessel segment is represented as an edge in the graph and has local features such as length, diameter, and direction, and relational features representing the connectivity of the vessel segments. Using these features, each edge in the graph is automatically labeled with its anatomical name using a stochastic relaxation algorithm. We have validated our method on micro-CT images of C57Bl/6J mice. A leave-one-out test performed on the labeled data set demonstrated the recognition rate for all vessels including major named vessels and their minor branches to be >75%. This automatic segmentation and recognition methods facilitate the comparison of blood vessels in large populations of subjects and allow us to study cerebrovascular variations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".