Automated registration and stitching of multiple 3D ultrasound images for monitoring neonatal intraventricular hemorrhage
Bibliographic record
Abstract
Dilatation of the cerebral ventricles is a common condition in preterm neonates with intraventricular hemorrhage (IVH). Post Hemorrhagic Ventricular Dilatation (PHVD) can lead to lifelong neurological impairment caused by ischemic injury due to increased intracranial pressure, and without treatment can lead to death. Previously, we have developed and validated a 3D ultrasound (US) system to monitor the progression of ventricle volumes (VV) in IVH patients; however, many patients with severe PHVD have ventricles so large they cannot be imaged within a single 3D US image. This limits the utility of atlas based segmentation algorithms required to measure VV as parts of the ventricles are in separate 3D US images, and thus, an already challenging segmentation becomes increasingly difficult to solve. Without a more automated segmentation, the clinical utility of 3D US ventricle volumes cannot be fully realized due to the large number of images and patients required to validate the technique in a clinical trials. Here, we describe the initial results of an automated ‘stitching’ algorithm used to register and combine multiple 3D US images of the ventricles of patients with PHVD. Our registration results show that we were able to register these images with an average target registration error (TRE) of 4.25±1.95 mm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".