Lamina landmark detection in ultrasound images: a preliminary study
Bibliographic record
Abstract
Freehand (FH) 3D ultrasound (US) imaging is emerging as a promising modality for spine imaging because it is non-invasive and inexpensive. Among the vertebral landmarks that can be used to represent the spine, paired laminae can play a vital role in a transverse scan for 3D spine deformity analysis by providing symmetry information. However, there is currently no laminae landmark recognition algorithm that has been tested on poor-quality 2D US scans. In this study, we propose a deep learning framework to automatically and simultaneously assess the presence of two laminae and estimate their landmark coordinates for the purpose of live US-based assessment of spine shape. To label the training data, we propose a labeling protocol based on a weight distribution on the virtual bone surface to make the pixel representative most likely the closest pixel to the spinal cord. In total, 6 FH 3D US sequences of the spine covering vertebrae T1 to L5 were collected from 3 participants. They were labeled based on the proposed protocol and validated by two spine ultrasound experts. The performance of the deep learning-based lamina landmark detection method was assessed through K-Fold cross-validation, with results reaching a mean distance error of 2.1 ± 1.3(mm) and 1.8 ± 1.2(mm) in true-positive images for left and right lamina landmarks respectively. Our method could allow for live laminae landmark extraction during clinical US spine exams, which would be useful for spinal ultrasound image interpretation, vertebral level identification, and spine deformity analysis in 3D based on paired laminae landmarks projected on three anatomical planes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".