Automatic Detection of Lumbar Anatomy in Ultrasound Images of Human Subjects
Bibliographic record
Abstract
Ultrasound has been proposed for aiding epidural needle insertion, but challenges remain in differentiating spinal structures due to noise, artifacts, and inexperience by anesthesiologists in ultrasound interpretation. Moreover, the anesthesiologist needs to measure relevant distances while preserving sterile conditions; therefore, interaction with the ultrasound controls must be minimal. Automated measurement is needed. Beam-steered ultrasound images are captured and spatial compounding is used to improve image quality. Phase symmetry is used to enhance bone (lamina) and ligamentum flavum (LF) ridges. A lamina template is matched to this ridge map using Pearson's cross-correlation, and the most likely lamina positions are found. Then, the lamina is traversed using a LF template with the Pearson's cross-correlation, and the location of the LF is obtained. Tests are performed on 39 sets of compounded ultrasound images in the L2-3 and L3-4 levels of the spine in the paramedian plane. The proposed algorithm can detect the laminas in 38 of the 39 images, and the LF in 34 of the 39 images. In successful detections, the automatic detections versus manual segmentation has an rms error of 0.64 mm and average error 0.04 mm, versus independent sonographer-measured depth has a root-mean-squared error of 3.7 mm and average error 2.5 mm, and versus the actual needle insertion depth has a root-mean-squared of 5.1 mm and average error -2.8 mm. The computational time is 4.3 s on a typical personal computer. The accuracy, reliability, and speed suggest this method may be valuable for helping guide epidurals in conjunction with the traditional loss-of-resistance method.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".