Standardization of blood flow measurements by automated vascular analysis from power Doppler ultrasound scan
Open accessAbstract
Power Doppler ultrasound imaging provides a non-invasive method to explore tissue vascularity in real-time. It has farreaching clinical utility, for example to assess the perfusion of organs such as the placenta. In this study, a fully automated method was proposed to standardize the power Doppler signals in the placental bed to estimate the perfusion by a technique known as fractional moving blood volume (FMBV). The uterine vasculature was extracted by a region growing algorithm from 3D power Doppler scan and further localized based on the placenta segmentation obtained by a multi-class fully Convolution Neural Network (CNN). The largest vessel close to the volume of interest (VOI) in placenta was identified within which the average power Doppler signal was used as a standardization value to correct the signal attenuation in placenta bed to estimate the FMBV measurement. This is the first successful attempt to automatically identify individual blood vessel segments from a complex uterine vascular plexus. The proposed method was performed in twenty 3D power Doppler scans of first trimester placenta with promising results. The mean ± STD FMBV value was 21.35% ± 9.43%. With further analysis and evaluation in large dataset, the proposed method will serve as an efficient tool for assessing the blood perfusion in placenta bed.
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3D ultrasound file reading and coordinate transformations
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Deep Learning Strategies for Ultrasound in Pregnancy
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Self-citationThe human placenta project: Funded studies, imaging technologies, and future directions
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Yin, Y., Clark, A. R., & Collins, S. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3376668 · Cited by 5 · see citing works
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- Authors
- Yin, Y., Looney, P., & Collins, S. L.
- Affiliations
- University of Oxford (GB)
- John Radcliffe Hospital (GB)
- Venue
- Medical Imaging 2020: Computer-Aided Diagnosis
- Publisher
- SPIE
- Published
- 2020-03-16
- Volume
- 11314
- Pages
- 154
- PMC
- PMC7594577
- OpenAlex
- W3011212401
- Cited by
- 4
- References
- 6
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.23
- Type
- conference paper
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- SPIE Medical Imaging: Computer-Aided Diagnosis (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U01HD087209)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Keywords
- power Doppler, placental perfusion, fractional moving blood volume, region growing algorithm, placenta segmentation, convolutional neural networks, 3D ultrasound, first trimester
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