Enhanced segmentation and skeletonization for endovascular surgical planning
Bibliographic record
Abstract
Endovascular surgery is becoming widely deployed for many critical procedures, replacing invasive medical operations with long recovery times. However, there are still many challenges in improving the efficiency and safety of its usage, and reducing surgery time; namely, regular exposure to radiation, manual navigation of surgical tools, lack of 3D visualization, and lack of intelligent planning and automatic tracking of a surgical end-effector. Thus, our goal is to develop hardware and software components of a tele-operation system to alleviate the abovementioned problems. There are three specific objectives in this project: (i) to reduce the need for a surgeon to be physically next to a patient during endovascular surgery; (ii) to overcome the difficulties encountered in manual navigation; and, (iii) to improve the speed and experience of performing such surgeries. To achieve (i) we will develop an electro-mechanical interface to accurately guide mechanically controlled surgical tools from a close distance, along with a 3D visualization interface; for (ii) we will replace the current surgical tools with an "intelligent wire" controlled by the electro-mechanical system; for (iii) we will segment 3D medical images to extract precise shapes of blood vessels, following which we will perform automatic path planning for a surgical end-effector.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".