Towards Automatic Semantic Segmentation in Volumetric Ultrasound
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There have been semi-automated and fully automated methods proposed for placental segmentation from 3D ultrasound, with varying results.
“There have been some semi-automated and fully automated methods proposed for the segmentation task, with varying results [10,11,12,13,14,15,16,17].”
From Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants (2022) · cites this paper as [17]
The Dice results from the proposed segmentation method were higher than those of other fully automatic methods.
“Our Dice results from the segmentation task were higher than other alternatives for fully automatic methods [11,12,13,14,17].”
From Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants (2022) · cites this paper as [17]
CNNs have found success in many computer vision tasks and are now being applied by research groups for segmentation of the heart.
“CNN have found success in many computer vision tasks and are now being applied by research groups for segmentation of the heart.”
From Convolutional Neural Networks for Automated Fetal Cardiac Assessment using 4D B-Mode Ultrasound (2019) · cites this paper as [6]
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Deep Learning in Medical Ultrasound Analysis: A Review
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Convolutional Neural Networks for Automated Fetal Cardiac Assessment using 4D B-Mode Ultrasound
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Combining Deep Learning and Multi-atlas Label Fusion for Automated Placenta Segmentation from 3DUS
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Torrents-Barrena, J., Piella, G., Masoller, N., Gratacos, E., Eixarch, E., Ceresa, M., & Ballester, M. A. G. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) · doi:10.1109/isbi.2019.8759296 · Cited by 9 · see citing works
- Authors
- Yang, X., Yu, L., Li, S., Wang, X., Wang, N., Qin, J., Ni, D., & Heng, P.-A.
- Affiliations
- Chinese University of Hong Kong (HK)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Shenzhen University (CN)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Ni, Dong
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 711-719
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2750941149
- Cited by
- 61
- References
- 16
- Citations by year
- 2024: 4 · 2023: 4 · 2022: 7 · 2021: 6 · 2020: 12 · 2019: 17 · 2018: 10 · 2017: 1
- FWCI
- 3.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- volumetric ultrasound imaging, 3D semantic segmentation, prenatal ultrasound, placenta segmentation, recurrent neural networks, boundary uncertainty, fetal monitoring
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