Deep Regression Segmentation for Cardiac Bi-Ventricle MR Images
Bibliographic record
Abstract
Cardiac bi-ventricle segmentation can help physicians to obtain clinical indices, such as mass and volume of left ventricle (LV) and right ventricle (RV). In this paper, we propose a regression segmentation framework to delineate boundaries of bi-ventricle from cardiac magnetic resonance (MR) images by building a regression model automatically and accurately. First, we extract DAISY feature from images. Then, a point based representation method is employed to depict the boundaries. Finally, we use DAISY as input and boundary points as labels to train the regression model based on deep belief network. Regression combined deep learning and DAISY feature can capture high level image information and accurately segment biventricle with fewer assumptions and lower computational cost. In our experiment, the performance of the proposed framework is compared with manual segmentation on 145 clinical subjects (2900 images in total), which are collected from three hospitals affiliated with two health care centers (London Healthcare Center and St. Josephs HealthCare). The results of our method and manually segmented method are highly consistent. High Pearson's correlation coefficient between automated boundaries and manual annotation is up to 0.995 (endocardium of LV), 0.997 (epicardium of LV), and 0.985 (RV). Average Dice metric is up to 0.916 (endocardium of LV), 0.941 (epicardium of LV), and 0.844 (RV). Altogether, experimental results are capable of demonstrating the efficacy of our regression segmentation framework for cardiac MR images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".