High-Resolution Ultrasound Data for AI-Based Segmentation in Mouse Brain Tumor
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most aggressive type of brain cancer, making effective treatments essential to improve patient survival. To advance the understanding of GBM and develop more effective therapies, preclinical studies commonly use mouse models due to their genetic and physiological similarities to humans. In particular, the GL261 mouse glioma model is employed for its reproducible tumor growth and ability to mimic key aspects of human gliomas. Ultrasound imaging is a valuable modality in preclinical studies, offering real-time, non-invasive tumor monitoring and facilitating treatment response assessment. Furthermore, its potential therapeutic applications, such as in tumor ablation, expand its utility in preclinical studies. However, real-time segmentation of GL261 tumors during surgery introduces significant complexities, such as precise tumor boundary delineation and maintaining processing efficiency. Automated segmentation offers a solution, but its success relies on high-quality datasets with precise labeling. Our study introduces the first publicly available ultrasound dataset specifically developed to improve tumor segmentation in GL261 glioblastomas, providing 1,856 annotated images to support AI model development in preclinical research. This dataset bridges preclinical insights and clinical practice, laying the foundation for developing more accurate and effective tumor resection techniques.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".