CT Imaging of Abdominal Aortic Aneurysms: Semi-Automatic Vessel Wall Detection and Quantification of Wall Thickness
Bibliographic record
Abstract
Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may provide useful information to predict rupture risk. Our procedure for estimating wall thickness in AAAs includes medical image segmentation and wall thickness detection. Image segmentation requires identifying and segmenting the luminal and outer wall boundaries of the blood vessels and wall thickness can be calculated by using intensity histograms and neural networks. The goal of this study is to develop an image-based, semi-automated method to trace the contours of the vessel wall and measure the wall thickness of the abdominal aorta from in-vivo, contrast-enhanced, CT images. An algorithm for the lumen and inner wall segmentations, and wall thickness detection was developed and tested on 10 ruptured and 10 unruptured AAAs. Reproducibility and repeatability of the algorithm were determined by comparing manual tracings made by two observers to contours made automatically by the algorithm itself. There was a high correspondence between automatic and manual area measurements for the lumen (r = 0.96) and between users (r = 0.98). Based on statistical analyses, the algorithm tends to underestimate the lumen area when compared to both observers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".