VWV-SSL: Carotid vessel-wall-volume segmentation via sequence structural similarity and augmentation consistency-based self-supervised learning
Bibliographic record
Abstract
Vessel wall volume (VWV) is a critical three dimensional ultrasound metric used to assess the progression and regression of carotid atherosclerosis. Ac curate measurement of VWV requires the segmentation of the media-adventitia boundary (MAB) and the lumen intima boundary (LIB) of the carotid arteries. Although deep learning methods can automatically segment the MAB and LIB and quantify VWV, they rely heavily on a large dataset with annotated images for training, which is time consuming and labor-intensive. Self-supervised learning (SSL) provides a possible solution to this challenge. However, existing SSL methods do not consider the similarities in the image sequences of 3D ultrasound. This paper proposes a novel SSL algorithm, named VWV-SSL, for 3D carotid ultrasound (3DUS) image segmentation to generate VWV measurement. VWV-SSL utilizes the sequence structural similarity and strong-weak augmented feature consistency of carotid ultrasound images to conduct the self-supervised task, which enables the networks to better learn the feature presentations of the vessel in the self-supervised task training. We applied VWV-SSL on the widely used 3D U-Net and evaluated it on 1158 3D US (579 of the common carotid artery and 579 of the bifurcation) from250subjects.Comparedtobaselinenetworks,our SSL method showed a significant improvement in segmentation performance when trained on a small number of labeled images (n = 15, 45 and 75 subjects). Moreover, the performance of VWV-SSL was superior to that of state-of-art SSL algorithms. These results indicate that our method can improve the performance of 3D U-Net when trained on a small number of labeled images, suggesting that VWV SSL could be applied in clinical practice to monitor the progression of atherosclerosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".