Bibliographic record
Abstract
Vessel analysis is important for a wide range of clinical diagnoses and disease research such as diabetes and malignant brain tumours. Vessel segmentation is a crucial first step in such analysis but is often complicated by structural diversity and pathology. Existing automated techniques have mixed results and difficulties with non-idealities such as imaging artifacts, tiny vessel structures, and regions with bifurcations. Live-Vessel is a novel and intuitive semi-automatic vessel segmentation technique that extends the classic Live-Wire technique from user-guided contours to user guided paths along vessel centre-lines with automated boundary detection. Live-Vessel achieves this by globally optimizing vessel filter responses over both spatial (x,y) and non-spatial (radius) variables simultaneously. In this thesis I provide three main contributions. First, I bring Live-Vessel into the domain of real-time interactivity. Second, I enhance the objective function for improved contrast and graph search performance by incorporating colour image information, adding penalty terms, utilizing a smaller data type, and increasing the contrast between desirable and undesirable paths. Third, I gather and retain vessel connectivity information and provide post-segmentation analysis tools. I validated this technique using real medical data from the DRIVE, STARE, and REVIEW retina vessel databases. Quantitative results show that, on average, Live-Vessel resulted in an 7.28 times reduction in overall manual segmentation task time at a 95% accuracy level with most radial and medial errors being under 1 pixel in distance.
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 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".