Frameless Registration Method Using a Depth Camera for Robot-Assisted Stereotactic Brain Surgery
Bibliographic record
Abstract
Stereotactic surgery aims to access critical areas of the brain with high accuracy.The classical surgical process requires two separate radiological imaging datasets (MRI-CT) and their precise registration.Additionally, specific anatomical landmarks (AC, PC, TAL) are manually identified by the neurosurgeon in both datasets, and MRI-CT registration is performed using these landmarks.To address the issues of patients' double exposure to radiological imaging and the manual identification of landmarks, this paper proposes a new approach based on the registration of facial landmarks.The proposed approach consists of four stages.The first stage involves creating 2D facial masks (MRHead and DHead) from the MRI and depth camera data obtained from the patient.Each mask, automatically generated using Google Mediapipe software, consists of 468 points.In the second stage, the mask points are transformed from 2D to 3D.In the third stage, precise registration of the 3D mask points is achieved using singular value decomposition (SVD) and random forest (RF) methods.In the final stage, using the registration matrix, the robotic arm is guided to reach the desired target point on a 3D-printed head prototype.Using the RF method for MHead and DHead mask registration, we obtained fiducial registration error (FRE) values of 1.633 mm and 1.523 mm, and target registration error (TRE) values of 2.217 mm and 2.164 mm for each patient, respectively.These promising results will form the basis of further developments in fully autonomous brain-targeting software with robotic assistance.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".