Left ventricle segmentation by combining convolution neural network with active contour model and tensor voting in short-axis MRI
Bibliographic record
Abstract
Left ventricle(LV) segmentation is a prerequisite step of evaluation of LV structure and function, which plays an important role in the diagnosis and treatment of cardiovascular diseases. In this paper, we propose a method to segment endocardium and epicardium of LV using convolution neural network combined with active contour model and tensor voting. A fully convolution neural network (FCN) named VGG16 is employed to segment myocardium of LV firstly. To improve the segmentation accuracy of endocardium, active contour model is employed to segment endocardium based on the initial segmentation results of FCN. Furthermore, to deal with the discontinuity of epicardium, tensor voting is used to fill the missing parts of myocardium. Finally, ellipse detection is employed to prune surplus parts in epicardium. Experiments on public datasets demonstrate that our method outperform most existed automated segmentation method in respect of several commonly used evaluation measures.
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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 | 0.001 |
| 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".