Subject-specific models for image-guided cardiac surgery
Bibliographic record
Abstract
Three-dimensional visualization for planning and guidance is still not routinely available for minimally invasive cardiac surgery (MICS). This can be addressed by providing the surgeon with subject-specific geometric models derived from 3D preoperative images for planning of port locations or to rehearse the procedure. For guidance purposes, these models can also be registered to the subject using intraoperative images. In this paper, we present a method for extracting subject-specific heart geometry from preoperative MR images. The main obstacle we face is the low quality of clinical data in terms of resolution, signal-to-noise ratio, and presence of artefacts. Instead of using these images directly, we approach the problem in three steps: (1) generate a high quality template model, (2) register the template with the preoperative data, and (3) animate the result over the cardiac cycle. Validation of this approach showed that dynamic subject-specific models can be generated with a mean error of 3.6+/-1.1 mm from low resolution target images (6 mm slices). Thus, the models are sufficiently accurate for MICS training and procedure planning. In terms of guidance, we also demonstrate how the resulting models may be adapted to the operating room using intraoperative ultrasound imaging.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".