A Novel Method for 3D-Segmentation of Vascular Images
Bibliographic record
Abstract
Constructing the accurate digital model of vessel networks is critical to vascular tissue engineering, in which the segmentation of vessel plays an important role. However, the existing segmentation methods are not able to achieve the goal of accurate segmentation of vessel networks. This paper presents the development of a method for vessel segmentation based on a data structure of octree and 3D region growing. Firstly, the volume data of vessel images are divided into different data groups according to the predetermined depth value of octree, and then the optimal slices sequence is defined by analyzing the octree’s nodes which contain the vessel region. Then, the vessel segmentation is conducted from the vessels images of octree nodes based on 3D region growing. Finally, the treated data blocks are reset and the segmentation results of the whole volume data are obtained. By applying this method to the volume data of vascular images from MRA, accurate vessel segmentation results are achieved. This work would represent a significant advance for digital modeling of vessel networks. Key Words: Vascular tissue engineering; Image segmentation; Region growing; Octree
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".