Rib Segmentation in Surgical Images for Video-Assisted Thoracoscopic Surgery
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer deaths worldwide. A potential early indicator of lung cancer is the presence of lung nodules that can be detected through screening. Open thoracotomy, a surgical approach for nodule resection, carries inherent risk which can be minimized with use of Video-Assisted Thoracoscopic Surgery (VATS); a minimally invasive alternative, reducing risks and recovery time. Precise nodule localization is crucial for efficient navigation during VATS. The utilization of intraoperative Cone-Beam Computed Tomography (CBCT), an imaging modality, can improve localization of the nodules. However, this poses a challenge when attempting to accurately align the nodule position from the CBCT to the surgical view. To address this, we propose a novel approach that segments corresponding features visible in both modalities, specifically the rib cages and Alexis O Wound Protector/Retractor (Alexis). The segmentation of these features is performed using YOLOv8 allowing image registration and alignment of the CBCT data with the surgical view. With the established correspondence, we can gauge possible camera locations and create an augmented reality overlay of the surgical site to provide real-time guidance in VATS.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".