Segmentation of the Left Ventricle for the Cardiac Phases between End-Diastole and End-Systole
Bibliographic record
Abstract
Automatic segmentation of cardiac structures, including the left ventricle (LV), is a crucial step for evaluating cardiac function. While deep learning is the leading approach for this task, the scarcity of labeled data presents a significant challenge. In scenarios with very limited labeled data, even semi-supervised learning methods may not be effective as they rely on the performance of an initially trained network. In this work, we propose a novel method for segmenting the LV contours for unlabeled cardiac phases between end-diastole (ED) and end-systole (ES). Our method leverages temporal coherence from the LV volume-time and shape information from the labeled cardiac phases to transform the ED and ES ground truth labels to the intervening cardiac phases, resulting in a larger set of labeled data. We evaluate our approach using three methods: 1- visual inspection, 2- using our method as data augmentation for training a CNN with limited ground truths, and 3- comparing results with fully supervised segmentation networks. Our method outperforms in all validation methods. Implementation is available at: "https://github.com/behnam-rahmati/LV-label-propagation".
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".