The sparse data extrapolation problem: strategies for soft-tissue correction for image-guided liver surgery
Bibliographic record
Abstract
The problem of extrapolating cost-effective relevant information from distinctly finite or sparse data, while balancing the competing goals between workflow and engineering design, and between application and accuracy is the 'sparse data extrapolation problem'. Within the context of open abdominal image-guided liver surgery, one realization of this problem is compensating for non-rigid organ deformations while maintaining workflow for the surgeon. More specifically, rigid organ-based surface registration between CT-rendered liver surfaces and laser-range scanned intraoperative partial surface counterparts resulted in an average closest-point residual 6.1 ± 4.5 mm with maximumsigned distances ranging from -13.4 to 16.2 mm. Similar to the neurosurgical environment, there is a need to correct for soft tissue deformation to translate image-guided interventions to the abdomen (e.g. liver, kidney, pancreas, etc.). While intraoperative tomographic imaging is available, these approaches are less than optimal solutions to the sparse data extrapolation problem. In this paper, we compare and contrast three sparse data extrapolation methods to that of datarich interpolation for the correction of deformation within a liver phantom containing 43 subsurface targets. The findings indicate that the subtleties in the initial alignment pose following rigid registration can affect correction up to 5- 10%. The best deformation compensation achieved was approximately 54.5% (target registration error of 2.0 ± 1.6 mm) while the data-rich interpolative method was 77.8% (target registration error of 0.6 ± 0.5 mm).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".