Revealing End-Systolic Right Ventricle Segmentation Strengths of EfficientNetB3 in DeepLabv3+
Bibliographic record
Abstract
Accurate segmentation of cardiac anatomical structures in cardiac magnetic resonance imaging (MRI) is vital for early diagnosis and treatment planning in cardiovascular diseases.In particular, the right ventricle (RV) during the end systolic (ES) phase is critical, as RV size is a strong indicator of cardiovascular health.Unlike the LV, the RV has a more complex geometry and thinner walls, making it difficult to delineate even manually.We propose to evaluate the performance of DeepLabv3+ using different backbone networks including EfficientNetB3, ResNet50, ResNet101, DesNet121, Xception, InceptionV3, VGG16, and VGG19 for multi-class segmentation of left ventricle (LV), right ventricle (RV), and myocardium (MYO).EfficientNetB3 as a backbone architecture in DeepLabv3+ outperformed with average score of Dice (0.913) and Jaccard (0.84).Moreover, it demonstrated the best performance in segmenting the RV during the challenging end-systolic phase structure often misclassified as MYO.This highlights the clinical potential of EfficientNetB3-integrated DeepLabv3+ for end-systolic challenging RV delineation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".