Real-time mitral annulus segmentation from 4D transesophageal echocardiography using deep learning regression
Bibliographic record
Abstract
Segmentation of the mitral annulus is an important step in many cardiac applications. Current methods to delineate the mitral annulus often require extensive user interaction. Several methods have been proposed to automate mitral annulus segmentation, but often use methods which require sampling 2D planes from the 3D volume, discarding some of the contextual information contained in the original 3D volume. We propose a new 4D mitral annulus segmentation method based on 3D CNN regression of Fourier coefficients describing the shape of predicted annulus. Our model predicts a set of ten coefficients for each of the three image axes, which can then be used to sample annulus coordinates through the inverse Fourier transform. We acquired a dataset of 90 cases from diagnostic imaging of mitral valve patients, with corresponding annulus segmentations. This was split into training, validation and test sets of 75, 5, and 10 cases respectively. Following training, our model achieves a curve-to-curve accuracy of 5.5 ± 2.2 mm on the test set, with training accuracy of 0.46 ± 0.21 mm. Our model achieves accuracy similar to current state-of-the-art methods, and can achieve inference speed of 40 frames-per-second, which is suitable for use in real-time image guidance applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".