Combining Deep Learning and Multi-atlas Label Fusion for Automated Placenta Segmentation from 3DUS
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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 [14]
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 [14]
Alternative methods based on deep learning have been proposed for placental segmentation.
“and a few alternative methods based on the recently popular deep learning methods have been proposed. 11 -14”
From Minimally interactive placenta segmentation from three-dimensional ultrasound images (2020) · cites this paper as [14]
The Oguz et al. 14 method combines deep learning techniques and the multi-atlas approach, unlike the currently presented method which only uses the multi-atlas component.
“This method 14 combines deep learning techniques and the multiatlas approach, unlike the currently presented method which only uses the multi-atlas component.”
From Minimally interactive placenta segmentation from three-dimensional ultrasound images (2020) · cites this paper as [14]
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Self-citation
- Authors
- Oguz, B. U., Wang, J., Yushkevich, N., Pouch, A., Gee, J., Yushkevich, P. A., Schwartz, N., & Oguz, I.
- Affiliations
- University of Pennsylvania (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 138-148
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2890317607
- Semantic Scholar
- cd227816d6b64da2f545063cfebb95dbaf19e774
- Cited by
- 13
- References
- 17
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 5 · 2019: 1
- FWCI
- 0.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- placenta segmentation, 3D ultrasound, multi-atlas label fusion, convolutional neural networks, automated medical image segmentation, first trimester
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