A deep learning-based approach for localization of pedicle regions in preoperative CT scans
Bibliographic record
Abstract
Pedicle screw fixation is a common yet technically demanding procedure. Due to the proximity of the inserted implant to the spinal column, a malplaced screw can cause neurological injury and subsequent postoperative complications. A common surgical routine starts with preoperative volumetric image acquisition (e.g. computed tomography) based on which the surgeons can highlight the planned trajectory. This process is generally done manually , which is error prone and time consuming. The primary purpose of this paper is to develop an automatic pedicle region localization based on preoperative CTs. This system can automatically annotate the CT scans to identify the regions corresponding to the pedicles and thus provide important information about the anatomical placement of the CT scan that can be useful for intraoperative implant position assessment (e.g. to initialize the 2D-3D registration). On the other hand, the pedicle localization can be exploited for preoperative planning. We designed and evaluated a fully convolutional neural network for the task of pedicle localization. A large training, validation and testing datasets (5000, 1000, 1000 images separately) were created using a custom data augmentation process that could generate unique vertebral morphologies for each image. After evaluation on the validation and test data, the Dice similarity coefficients between the pedicle regions detected by the trained network and the ground truth was 0.85 and 0.83 respectively. The proposed deep-learning-based algorithm was capable of automatically localizing the regions corresponding to the pedicles based on the preoperative CT scans. Therefore, a reliable initial guess for the 2D-3D registration process needed for intraoperative implant position assessment can be achieved. This system also has potential use in automating the preoperative planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".