Automatic Segmentation of the Left Ventricle Through the Cardiac Cycle in Pediatric Echocardiography Videos Using SegFormer Architecture
Bibliographic record
Abstract
Echocardiography generates real-time results, aiding in examination and diagnosis. It is widely used for detecting congenital heart disease (CHD), evaluating risk, and guiding treatment strategies in pediatric cardiology. However, the complexity of these images makes their interpretation and analysis challenging, often leading to inter-observer variability. This research aims to develop an automated left ventricle (LV) segmentation method throughout the full cardiac cycle for pediatric echocardiography videos using a semantic Transformer model known as SegFormer. The goal is to support the analysis of clinical imaging techniques. In recent years, semantic Transformers have demonstrated significant effectiveness in segmentation tasks, making them highly suitable choice for this application. To achieve accurate LV segmentation through the cardiac cycle, the SegFormer model is trained using the EchoNet-Peds dataset, which consists of annotated pediatric echocardiography videos. The initial training phase includes segmenting the left ventricle images at the end of systole and the end of diastole, with performance evaluated based on accuracy, mean absolute error (MAE), recall and Dice score metrics to compare with other pediatric segmentation methods. As a final result, this research produces segmented left ventricle videos throughout the cardiac cycle for different pediatric echocardiography videos.Clinical RelevanceBy applying a semantic Transformer to pediatric echocardiography for automated LV segmentation, the quantification of key cardiac parameters such as ejection fraction, end of diastole, and end of systole is improved, leading to greater accuracy and providing more valuable information for medical staff. Consequently, a tool capable of efficiently processing large volumes of data can significantly facilitate and support the diagnosis process for pediatric patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".