Ultrasound-based technique for intrathoracic surgical guidance
Bibliographic record
Abstract
Image-guided procedures within the thoracic cavity require accurate registration of a pre-operative virtual model to the patient. Currently, surface landmarks are used for thoracic cavity registration; however, this approach is unreliable due to skin movement relative to the ribs. An alternative method for providing surgeons with image feedback in the operating room is to integrate images acquired during surgery with images acquired pre-operatively. This integration process is required to be automatic, fast, accurate and robust; however inter-modal image registration is difficult due to the lack of a direct relationship between the intensities of the two image sets. To address this problem, Computed Tomography (CT) was used to acquire pre-operative images and Ultrasound (US) was used to acquire peri-operative images. Since bone has a high electron density and is highly echogenic, the rib cage is visualized as a bright white boundary in both datasets. The proposed approach utilizes the ribs as the basis for an intensity-based registration method -- mutual information. We validated this approach using a thorax phantom. Validation results demonstrate that this approach is accurate and shows little variation between operators. The fiducial registration error, the registration error between the US and CT images, was < 1.5mm. We propose this registration method as a basis for precise tracking of minimally invasive thoracic procedures. This method will permit the planning and guidance of image-guided minimally invasive procedures for the lungs, as well as for both catheter-based and direct trans-mural interventions within the beating heart.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".