Multimodal μCT/μMR based semiautomated segmentation of rat vertebrae affected by mixed osteolytic/osteoblastic metastases
Bibliographic record
Abstract
PURPOSE: Multimodal microimaging in preclinical models is used to examine the effect of spinal metastases on bony structure; however, the evaluation of tumor burden and its effect on microstructure has thus far been mainly qualitative or semiquantitative. Quantitative analysis of multimodality imaging is a time consuming task, motivating automated methods. As such, this study aimed to develop a low complexity semiautomated multimodal μCT/μMR based approach to segment rat vertebral structure affected by mixed osteolytic/osteoblastic destruction. METHODS: Mixed vertebral metastases were developed via intracardiac injection of Ace-1 canine prostate cancer cells in three 4-week-old rnu/rnu rats. μCT imaging (for high resolution bone visualization), T1-weighted μMR imaging (for bone registration), and T2-weighted μMR imaging (for osteolytic tumor visualization) were conducted on one L1, three L2, and one L3 vertebrae (excised). One sample (L1-L3) was processed for undecalcified histology and stained with Goldner's trichome. The μCT and μMR images were registered using a 3D rigid registration algorithm with a mutual information metric. The vertebral microarchitecture was segmented from the μCT images using atlas-based demons deformable registration, levelset curvature evolution, and intensity-based thresholding techniques. The μCT based segmentation contours of the whole vertebrae were used to mask the T2-weighted μMR images, from which the osteolytic tumor tissue was segmented (intensity-based thresholding). RESULTS: Accurate registration of μCT and μMRI modalities yielded precise segmentation of whole vertebrae, trabecular centrums, individual trabeculae, and osteolytic tumor tissue. While the algorithm identified the osteoblastic tumor attached to the vertebral pereosteal surfaces, it was limited in segmenting osteoblastic tissue located within the trabecular centrums. CONCLUSIONS: This semiautomated segmentation method yielded accurate registration of μCT and μMRI modalities with application to the development of mathematical models analyzing the mechanical stability of metastatically involved vertebrae and in preclinical applications evaluating new and existing treatment effects on tumor burden and skeletal microstructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".