E-024 Deep learning-based cerebral aneurysm segmentation and morphological analysis on the three-dimensional rotational angiography
Bibliographic record
Abstract
Background Morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device selection in the endovascular treatment of cerebral aneurysms, but manual evaluation by human raters has only moderate inter-/intra-rater reliability. Purpose To develop and evaluate the performance of an automatic morphological analysis tool for cerebral aneurysms, which is based on a combination of deep learning and rule-based image processing algorithms. Materials and Methods Cerebral angiography data from 889 consecutive patients with suspected cerebral aneurysms were retrospectively collected at our institution from January 2017 to October 2021. The automatic morphological analysis model was trained and developed on the derivation cohort dataset consisting of 388 scans with 437 aneurysms, and the performance of the model was tested on the validation cohort dataset consisting of 96 scans with 124 aneurysms. Five clinically important parameters were automatically calculated by the model; aneurysm volume, maximum aneurysm size, neck size, aneurysm height, and aspect ratio. Results On the validation cohort dataset, the average aneurysm size was 7.9±4.6 mm. The proposed model displayed high detection and segmentation accuracy with lesion-level sensitivity of 98.4%, false positives per scan of 0.21, and mean Dice similarity index of 0.87 (median 0.93). All the morphological parameters were significantly correlated with the reference standard (all p<0.0001; Pearson correlation analysis). Among the aneurysms, the model could discriminate aneurysms smaller than 7 mm with sensitivity 90.7%, specificity 100.0%, and area under curve of 0.95, and wide-neck aneurysms (neck size larger than 4 mm) with sensitivity 85.7%, specificity 88.5%, and area under curve of 0.87. Conclusions The automatic aneurysm analysis model based on angiography data had high accuracy on evaluating the morphological characteristics of cerebral aneurysms, which might help planning strategy and selecting devices for endovascular treatment of cerebral aneurysms. Disclosures H. Nishi: None. A. Lustici: None. N. Cancelliere: None. T. Marotta: None. J. Spears: None. V. Pereira: None.
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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 | 0.001 | 0.000 |
| 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) | 0.008 | 0.003 |
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".