Deep Learning-Based Analysis of Aortic Morphology From Three-Dimensional MRI
© 2024 International Society for Magnetic Resonance in Medicine..
BACKGROUND: Quantification of aortic morphology plays an important role in the evaluation and follow-up assessment of patients with aortic diseases, but often requires labor-intensive and operator-dependent measurements. Automatic solutions would help enhance their quality and reproducibility.
PURPOSE: To design a deep learning (DL)-based automated approach for aortic landmarks and lumen detection derived from three-dimensional (3D) MRI.
STUDY TYPE: Retrospective.
POPULATION: Three hundred ninety-one individuals (female: 47%, age = 51.9 ± 18.4) from three sites, including healthy subjects and patients (hypertension, aortic dilation, Turner syndrome), randomly divided into training/validation/test datasets (N = 236/77/78). Twenty-five subjects were randomly selected and analyzed by three operators with different levels of expertise.
FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T, 3D spoiled gradient-recalled or steady-state free precession sequences.
ASSESSMENT: Reinforcement learning and a two-stage network trained using reference landmarks and segmentation from an existing semi-automatic software were used for aortic landmark detection and segmentation from sinotubular junction to coeliac trunk. Aortic segments were defined using the detected landmarks while the aortic centerline was extracted from the segmentation and morphological indices (length, aortic diameter, and volume) were computed for both the reference and the proposed segmentations.
STATISTICAL TESTS: Segmentation: Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetrical surface distance (ASSD); landmark detection: Euclidian distance (ED); model robustness: Spearman correlation, Bland-Altman analysis, Kruskal-Wallis test for comparisons between reference and DL-derived aortic indices; inter-observer study: Williams index (WI). A WI 95% confidence interval (CI) lower bound >1 indicates that the method is within the inter-observer variability. A P-value <0.05 was considered statistically significant.
RESULTS: DSC was 0.90 ± 0.05, HD was 12.11 ± 7.79 mm, and ASSD was 1.07 ± 0.63 mm. ED was 5.0 ± 6.1 mm. A good agreement was found between all DL-derived and reference aortic indices (r >0.95, mean bias <7%). Our segmentation and landmark detection performances were within the inter-observer variability except the sinotubular junction landmark (CI = 0.96;1.04).
DATA CONCLUSION: A DL-based aortic segmentation and anatomical landmark detection approach was developed and applied to 3D MRI data for achieve aortic morphology evaluation.
EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Journal of magnetic resonance imaging : JMRI - (2024) vom: 12. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guo, Jia [VerfasserIn] |
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Date Revised 12.01.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1002/jmri.29236 |
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| PPN (Katalog-ID): |
NLM367070952 |
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| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024 International Society for Magnetic Resonance in Medicine. | ||
| 520 | |a BACKGROUND: Quantification of aortic morphology plays an important role in the evaluation and follow-up assessment of patients with aortic diseases, but often requires labor-intensive and operator-dependent measurements. Automatic solutions would help enhance their quality and reproducibility | ||
| 520 | |a PURPOSE: To design a deep learning (DL)-based automated approach for aortic landmarks and lumen detection derived from three-dimensional (3D) MRI | ||
| 520 | |a STUDY TYPE: Retrospective | ||
| 520 | |a POPULATION: Three hundred ninety-one individuals (female: 47%, age = 51.9 ± 18.4) from three sites, including healthy subjects and patients (hypertension, aortic dilation, Turner syndrome), randomly divided into training/validation/test datasets (N = 236/77/78). Twenty-five subjects were randomly selected and analyzed by three operators with different levels of expertise | ||
| 520 | |a FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T, 3D spoiled gradient-recalled or steady-state free precession sequences | ||
| 520 | |a ASSESSMENT: Reinforcement learning and a two-stage network trained using reference landmarks and segmentation from an existing semi-automatic software were used for aortic landmark detection and segmentation from sinotubular junction to coeliac trunk. Aortic segments were defined using the detected landmarks while the aortic centerline was extracted from the segmentation and morphological indices (length, aortic diameter, and volume) were computed for both the reference and the proposed segmentations | ||
| 520 | |a STATISTICAL TESTS: Segmentation: Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetrical surface distance (ASSD); landmark detection: Euclidian distance (ED); model robustness: Spearman correlation, Bland-Altman analysis, Kruskal-Wallis test for comparisons between reference and DL-derived aortic indices; inter-observer study: Williams index (WI). A WI 95% confidence interval (CI) lower bound >1 indicates that the method is within the inter-observer variability. A P-value <0.05 was considered statistically significant | ||
| 520 | |a RESULTS: DSC was 0.90 ± 0.05, HD was 12.11 ± 7.79 mm, and ASSD was 1.07 ± 0.63 mm. ED was 5.0 ± 6.1 mm. A good agreement was found between all DL-derived and reference aortic indices (r >0.95, mean bias <7%). Our segmentation and landmark detection performances were within the inter-observer variability except the sinotubular junction landmark (CI = 0.96;1.04) | ||
| 520 | |a DATA CONCLUSION: A DL-based aortic segmentation and anatomical landmark detection approach was developed and applied to 3D MRI data for achieve aortic morphology evaluation | ||
| 520 | |a EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 3D cardiac MRI | |
| 650 | 4 | |a aorta | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a segmentation | |
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