Sci-PM Thurs - 07: Registration of geometric cardiac models to magnetic resonance images
Bibliographic record
Abstract
Minimally invasive cardiac surgery (MICS) has already been shown to reduce hospital stays, but the full potential is not yet realized, largely due to limitations in pre- and intra-operative visualization inside the closed chest. To address these issues, we are developing the Virtual Cardiac Surgery Platform (VCSP) — a 4D (3D + time), virtual reality model of the patient specific thorax, derived from pre-procedural images. In this abstract, we discuss the accuracy of our image registration-based method for deforming geometrical template models of the heart sub-anatomy (myocardium, right atrium + ventricle, left atrium + aorta, epicardium) to 10 different volunteers (“patients”). The template models are built by manually segmenting a high quality magnetic resonance (MR) image (this template image is an average of 20 acquisitions of the same volunteer, 1.53 mm3 voxels). The template image is mapped to a much lower quality patient image (1.5×1.5×6.0 mm3 voxels) obtained in a clinically feasible manner, by maximizing the normalized mutual information (NMI) between the two images. The resulting global (affine) and local (free form deformation) transformation is applied to one of the four template models to transform it into patient space. The registration accuracy is assessed by comparing the mapped template to the manual segmentation of the patient. On average, the customization process is accurate to within 2.4 ± 0.2 mm, whereas, the difference between two manual segmentations (gold standards) was 1.3 ± 0.2 mm. We believe our method adequately prepares templates for use within VCSP, prior to and during MICS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".