Hip segmentation from MRI volumes in infants for DDH diagnosis and treatment planning
Bibliographic record
Abstract
Diagnosis and surgical management of Developmental Dysplasia of the Hip (DDH) relies on physical examination and 2D ultrasound scanning. Magnetic Resonance Imaging (MRI) can be used to complement existing techniques and could be advantageous in treatment planning due to its larger field of view. In this paper we propose a semi-automatic method to segment surface models of the acetabulum from MRI images. The method incorporates clinical knowledge in the form of intensity priors which are integrated into a Random Walker (RW) formulation. We use a modified RW framework which compensates for incomplete or blurred boundaries in the image by using information from neighboring slices in the sequence incorporated as node weights. We conducted a pilot study to evaluate the segmentation on a set of 10 infant hip MRI sequences using a 1.5 Tesla MR scanner. Contours obtained from the semi-automated segmentation were compared against manually segmented hip contours using Dice Ratio (DR), Hausdorff Distance (HD) and Root Mean Square (RMS) distance. The proposed method gave values of (DR = 0.84 ± 0.5, HD =3.0 ± 0.7, RMS =1.9 ± 0.3) and (DR=0.86 ± 0.2, HD=3.0 ± 0.1, RMS= 2.0 ± 0.6) for right and left acetabular contours respectively which was higher than the corresponding values obtained from conventional RW segmentation. The execution time of the segmentation algorithm was less than ~4 seconds on a 3.5 GHz CPU.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 0.000 |
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".