Fully Automated Analytical Segmentation of Animal Beds in Multi-Mouse PET/SPECT/CT Imaging with the Pre-Clinical VECTor6 Scanner
Bibliographic record
Abstract
The VECTor6 PET/SPECT/CT pre-clinical imaging system enables simultaneous scanning of up to four mice using a shared animal bed, commonly referred to as a “mouse hotel”. This is particularly advantageous for high-throughput studies. For effective downstream analysis, it is desirable to isolate animals into individual images, center them, and remove the bed structure. Removing the bed facilitates the generation of maximum intensity projections (MIPs) and ensures standardized subject orientation. However, the mouse hotel and bed components are made of acrylic, which has attenuation properties similar to soft tissue, making segmentation challenging. In this study, images were acquired from five groups of four mice, each group imaged twice with a 24 -hour interval, resulting in 10 acquisitions and 40 individual mouse images. Axial rotational alignment was achieved using the 2D Radon transform, taking advantage of the mouse hotel's geometry and optimizing for peak values. Bed segmentation exploited material attenuation differences and implemented through multiple masks combined with connected component analysis to detect and remove bed structures. Manually segmented CT images were used as ground truth for validation. The automated method achieved an average Dice coefficient of 0.978 [0.972-0.984], Jaccard index of 0.958 [0.945-0.968], accuracy of 0.989 [0.984-0.992], precision of 0.959 [0.946-0.968], and recall of 0.999 [0.996-1.000]. Deviations primarily occurred near the snout, where isoflurane is delivered, and the ears, which were inconsistently captured. Overall, the results demonstrate a high degree of concordance with manual segmentation, while reducing processing time from 30 minutes to 3 minutes per scan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".