Bibliographic record
Abstract
Segmentation of secondary brain tumours enables clinicians to plan clinical interventions. This task is currently done manually, which is time consuming and susceptible to inter-observer variability. Automating the segmentation is challenging due to the varying size, number, and heterogeneity of the lesions. Presently, 1 W post-Gadolinium images are used by clinicians for manual segmentation. This approach exposes the patient to gadolinium (Gd), which has been found to accumulate in healthy tissue and could potentially have long term health effects. To avoid this and address the need for a reliable, fast, and reproducible segmentation approach, we present a deep-learning-based automatic segmentation algorithm from magnetization transfer ratio (MTR) images, Gd-enhanced T1-weighted images (T1w-Gd), and T2-weighted Fluid-Attenuated Inversion Recovery (FLAIR) images. A comparison between the various combinations of imaging modalities indicate that automatic tumour, and tumour sub-region segmentation is doable with deep learning, yet the accuracy is limited, likely due to the small dataset and the heterogeneity of the tumours. Additionally, the use of gadolinium as an exogenous contrast agent remains necessary while automatic segmentation using MTR continues to be investigational.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".