The cerebral arterial tree segmentation software
Bibliographic record
Abstract
Abstract: Time-of-flight magnetic resonance angiography (TOF-MRA) is a type of magnetic resonance imaging (MRI) technique that allows visualization of the brain's arterial structure. Over the past few years, deep learning, which falls under the umbrella of artificial intelligence, has gained widespread adoption in the medical imaging field. Among its many applications, deep learning has proven to be beneficial for identifying and clasDsifying specific areas of interest on medical images including the cerebral vasculature. This thesis illustrates how deep learning can be utilized to generate an accurate multi-class segmentation of the cerebral arterial tree. Using deep learning, we created the cerebral arterial tree segmentation software (CATSS). CATSS is the first fully automated software tool capable of segmenting the whole cerebral arterial tree in TOF-MRA quickly, accurately, reliably and in a multi-class fashion. Thus, distinguishing between the different arteries of the arterial tree and classifying them into different classes. This work would enable a quantitative evaluation of a TOF-MRA of the brain going beyond the traditional visual assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.012 | 0.005 |
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".