Machine Learning and Graph Based Approach to Automatic Right Atrial Segmentation from Magnetic Resonance Imaging
Bibliographic record
Abstract
Manual delineation of the right atrium throughout the cardiac cycle is tedious and time-consuming, yet promising for early detection of right heart dysfunction. In this study, we developed a fully automated approach to right atrial segmentation in 4-chamber long-axis magnetic resonance image (MRI) cine sequences by applying a U-Net based neural network approach followed by a contour reconstruction and refinement algorithm. In contrast to U-Net, the proposed approach performs segmentation using open contours. This allows for exclusion of the tricuspid valve region from the atrial segmentation, an essential aspect in the analysis of atrial wall motion. The MR images were retrospectively acquired from 242 cine sequences which were manually segmented by an expert radiologist to produce the ground truth data. The neural network was trained over 600 epochs under six different hyperparameter configurations on 202 randomly selected sequences to recognize a dilated region surrounding the right atrial contour. A graph algorithm is then applied to the binary labels predicted by the trained model to accurately reconstruct the corresponding contours. Finally, the contours are refined by combining a nonrigid registration algorithm which tracks the deformation of the heart and a Gaussian process regression. Evaluation of the proposed method on the remaining 40 MR image sequences excluding a single outlier sequence yielded promising Sørensen-Dice coefficients and Hausdorff distances of 95.2% and 4.64 mm respectively before refinement and 94.9% and 4.38 mm afterward.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".