Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Overview
2.2. Study Population
2.3. PC MRI Acquisition and Vessel Segmentation
2.4. Mathematical Description of UNets
2.5. Deep Learning Model Architecture, Training, and Testing
- ±15° rotation of the image;
- ±50 pixels image translation on the x and y axes;
- 0.7 to 1.3 times image scaling;
- ±15° image shear.
2.6. Flow Velocity and Volume Measurements
3. Statistical Analysis
4. Results
4.1. Segmentation Results
4.2. Flow and Velocity Measurements
5. Discussion
5.1. Performance of the Deep Learning Models
5.2. Flow Volume and Velocity Measurements
5.3. Comparison with Similar Studies on Image Segmentation
5.4. Implications of Results for General Readers
6. Limitations
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ACZ | acetazolamide |
AUC | area under the curve |
CBF | cerebral blood flow |
CNN | convolutional neural network |
DSC | Dice similarity coefficient |
HC | healthy control |
LECA | left external carotid artery |
LICA | left internal carotid artery |
LVA | left vertebral artery |
MM | moyamoya |
MRI | magnetic resonance imaging |
PC | phase contrast |
RECA | right external carotid artery |
RICA | right internal carotid artery |
ROI | region of interest |
ROC | receiver operating characteristic |
RVA | right vertebral artery |
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Parameters | Unit | Value |
---|---|---|
TR/TE | ms | 12.4/4.6 |
Flip angle | degrees | 20 |
FOV | mm | 180 × 180 |
Matrix | 512 × 512 | |
Voxel size | mm | 0.3516 × 0.3516 × 3 |
Cardiac phases | 10 | |
Average time per cardiac phase | ms | 88 |
Slice thickness | mm | 3 |
Number of slices | 1 | |
Velocity encoding | cm/s | 100 |
Repeats | 2 | |
Scan duration | min | 1:30 |
Healthy Control Baseline (n = 53) | Moyamoya Baseline (n = 16) | Healthy Control Diamox (n = 54) | Moyamoya Diamox (n = 30) | All Subjects in All Conditions (n = 153) | |
---|---|---|---|---|---|
U-Net | 0.92 ± 0.03 | 0.58 ± 0.31 | 0.92 ± 0.05 | 0.73 ± 0.18 | 0.81 ± 0.21 |
Attention U-Net | 0.87 ± 0.06 | 0.69 ± 0.21 | 0.90 ± 0.05 | 0.76 ± 0.15 | 0.85 ± 0.13 |
Nested U-Net | 0.85 ± 0.11 | 0.79 ± 0.21 | 0.91 ± 0.06 | 0.80 ± 0.13 | 0.85 ± 0.14 |
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Share and Cite
Campbell, B.; Yadav, D.; Hussein, R.; Jovin, M.; Hoover, S.; Halbert, K.; Holley, D.; Khalighi, M.; Davidzon, G.A.; Tong, E.; et al. Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks. Appl. Sci. 2023, 13, 11820. https://doi.org/10.3390/app132111820
Campbell B, Yadav D, Hussein R, Jovin M, Hoover S, Halbert K, Holley D, Khalighi M, Davidzon GA, Tong E, et al. Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks. Applied Sciences. 2023; 13(21):11820. https://doi.org/10.3390/app132111820
Chicago/Turabian StyleCampbell, Britney, Dhruv Yadav, Ramy Hussein, Maria Jovin, Sierrah Hoover, Kim Halbert, Dawn Holley, Mehdi Khalighi, Guido A. Davidzon, Elizabeth Tong, and et al. 2023. "Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks" Applied Sciences 13, no. 21: 11820. https://doi.org/10.3390/app132111820
APA StyleCampbell, B., Yadav, D., Hussein, R., Jovin, M., Hoover, S., Halbert, K., Holley, D., Khalighi, M., Davidzon, G. A., Tong, E., Steinberg, G. K., Moseley, M., Zhao, M. Y., & Zaharchuk, G. (2023). Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks. Applied Sciences, 13(21), 11820. https://doi.org/10.3390/app132111820