<title>Registration of 3D tracked ultrasonic spinal images to segmented CT images for technology-guided therapy</title>
Bibliographic record
Abstract
As a prerequisite to performing minimally-invasive spinal surgery (MISS) with technology-guided therapy (TGT), researchers at Vanderbilt University have proposed to mathematically align the physical space of the patient with preoperative images through a surface-based registration. In order to support closed-back spinal surgeries, we have selected a non-invasive, portable imaging modality for obtaining intra-operative images, namely ultrasound (U/S). The preliminary work for the application of TGT to spinal cases has been performed on a spine phantom, scanned with an optically-tracked U/S transducer. The lumbar vertebral surface was extracted from the U/S images, and the surface pixels were converted into 3D physical-space coordinates. This set of U/S surface points was divided into a test set and a target set to be used in registration and error measurement, respectively. The test set of U/S points was registered to segmented CT spinal images of the same phantom spine using a modification of the Besl-McKay Iterative Closest Point algorithm. In a qualitative analysis of the registration, the results look favorable. The U/S points closely align with the corresponding CT surface in every image slice. By incorporating TGT into minimally-invasive spinal surgeries, the procedures are expected to yield reduced injury to normal spinal tissue and hence quicker recovery time for the patient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".