Improving visualization of three-dimensional aneurysm features via segmentation with upsampled resolution and gradient enhancement (SURGE)
Bibliographic record
Abstract
BACKGROUND: Intracranial aneurysm neck width tends to be overestimated when measured with three-dimensional rotational angiography (3DRA) compared with two-dimensional digital subtraction angiography (2D-DSA), owing to high curvature at the neck. This may affect morphological and hemodynamic analysis in support of treatment planning. We present and validate a method for extracting high curvature features, such as aneurysm ostia, during segmentation of 3DRA images. METHODS: In our novel SURGE (segmentation with upsampled resolution and gradient enhancement) approach, the gradient of an upsampled image is sharpened before gradient-based watershed segmentation. Neck measurements were performed for both standard and SURGE segmentations of 3DRA for 60 consecutive patients and compared with those from 2D-DSA. Those segmentations were also qualitatively compared for surface topology and morphology. RESULTS: Compared with the standard watershed method, SURGE reduced neck measurement error relative to 2D-DSA by >60%: median error was 0.49 mm versus 0.17 mm for SURGE, which is less than the average pixel resolution (~0.33 mm) of the 3DRA dataset. SURGE reduced neck width overestimations >1 mm from 13/60 to 5/60 cases. Relative to 2D-DSA, standard segmentations were overestimated by 16% and 93% at median and 95th percentiles, respectively, compared with only 6% and 37%, respectively, for SURGE. CONCLUSION: SURGE provides operators with high-level control of the image gradient, allowing recovery of high-curvature features such as aneurysm ostia from 3DRA where conventional algorithms may fail. Compared with standard segmentation and tedious manual editing, SURGE provides a faster, easier, and more objective method for assessing aneurysm ostia and morphology.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".