Improving Right Ventricle Segmentation in Cardiac Magnetic Resonance Imaging Through Transfer Learning
Bibliographic record
Abstract
Segmentation of the right ventricle (RV) in magnetic resonance imaging (MRI) sequences is critical for assessing RV function. However, manual segmentation involves processing hundreds of images per patient, making it a tedious and timeconsuming process. Recently, deep convolutional neural networks have emerged as an effective solution for automating RV segmentation in MRI sequences, substantially reducing manual workload. Accurate segmentation of the RV is crucial for reliable clinical applications. In this study, we demonstrate that transfer learning using a pre-trained segmentation model from the Medical Open Network for Artificial Intelligence (MONAI) Model Zoo significantly improves segmentation accuracy, as measured by the Dice similarity coefficient (DSC) and$\mathbf{9 5}^{\text {th }}$percentile Hausdorff distance (HD95) scores, compared to manual annotations from medical experts. Our approach increased DSC-based segmentation accuracy from 74.93 % (pre-trained MONAI Zoo model) and 83.15 % (same architecture trained on our data) to 84.91 % on 1,994 test images acquired from seven patients. Furthermore, it outperformed a state-of-the-art self-configuring network, nnU-Net, which achieved an accuracy of 81.98 % on the same dataset. This study demonstrates the effectiveness of transfer learning in improving segmentation accuracy for the proposed task.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".