Automated aortic segmentation and quantification of hemodynamic parameters from 4D flow MRI using deep learning techniques
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Bibliographic record
Abstract
PurposeTo develop an automated method for aortic segmentation using deep learning techniques and further analyze the hemodynamic parameters in patients with bicuspid aortic valve (BAV). Since four-dimensional (4D) flow magnetic resonance imaging (MRI) imaging helps in analyzing and quantifying the blood flow changes that occur in aortic valve-related problems, such as BAV, 4D flow MRI images are considered.ApproachOur dataset consisted of 91 patients who had referral indications of BAV and 30 healthy volunteers who had no known cardiovascular disease. A U-Net++ with pretrained ResNet-34 encoders was trained for aortic segmentation using manual segmentation by an expert as the ground truth. In the first stage, the model was evaluated on 21 test cohorts using overlay and distance-based metrics, such as Dice score, Hausdorff distance, and absolute volume difference. In the second stage, the hemodynamic parameters, such as wall shear stress (WSS), viscous energy loss, and vorticity, were calculated to quantify the blood flow irregularities that occur in BAV patients. The segmentation and the flow parameters generated by the algorithm were compared with those generated using the manual segmentations. Paired t-test with alpha value of 0.05 was used for statistical significance testing.ResultsAs for overlap and distance-based metrics, the developed algorithm reported a Dice score coefficient of 0.90 ± 0.03, absolute volume difference of 1683 ± 1139 mm3, and Hausdorff distance of 3.2 ± 1.18 mm on test cohorts. The hemodynamic parameters calculated between automated and manual methods resulted in a mean difference of 6.62% for WSS with p-value of 0.94, 17.35% for mean viscous energy loss with p-value of 0.78, and 7.59% for vorticity with p-value of 0.97.ConclusionsA fast and accurate segmentation tool was developed for aortic segmentation using a dataset taken at clinical and blood flow parameters that were calculated based on the segmented aorta. These results will assist the clinicians to analyze the blood flow patterns and commence distinguished treatment in BAV patients.
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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 it