A Comparison of Deep Learning Models for Automatic Left-Ventricular Segmentation in 3D Echocardiography
Bibliographic record
Abstract
Echocardiography is a non-invasive, non-ionizing, and cost-effective medical imaging modality that uses ultrasound waves to evaluate cardiac function. Left ventricle (LV) analysis is crucial for diagnosing cardiac diseases. Segmentation of the LV from echocardiography images is a challenging, time-consuming process requiring manual contouring from experts and is prone to inter-observer variability. Five different deep-learning models are evaluated for automatic LV segmentation from 3D echocardiography. The models were compared using overlap and distance metrics: Dice score, Jaccard index, and Hausdorff distance. Volumetric analysis was used to examine the accuracy of the predictions from the deep learning models against the expert-annotated ground truth volumes. The comparison between these models provides a foundation for further development of accurate and efficient automated LV segmentation methods, particularly approaches that can leverage the temporal consistency of echocardiography scans.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".