Automatic staging of placental maturity based on dense descriptor
Open accessAbstract
Currently, placental maturity staging is mainly based on subjective observation of the physician. To address this issue, a new method is proposed for automatic staging of placental maturity based on B-mode ultrasound images. Due to small variations in the placental images, dense descriptor is utilized in place of the sparse descriptor to boost performance. Dense sampled DAISY descriptor is investigated for the demonstrated scale and translation invariant properties. Moreover, the extracted dense features are encoded by vector locally aggregated descriptor (VLAD) for performance boosting. The experimental results demonstrate an accuracy of 0.874, a sensitivity of 0.996 and a specificity of 0.874 for placental maturity staging. The experimental results also show that the dense features outperform the sparse features.
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The automated classification of placental maturity was proposed by Lei et al. and Li et al.
“The automated classification of placental maturity was proposed by Lei et al. and Li et al. [116,117].”
Existing methods for placental maturity grading often focus on designing hand-crafted features or combining deep and hand-crafted features with an SVM.
“The existing methods often focus on designing diverse hand-crafted features or combining deep features and handcrafted features to learn a hybrid feature with an SVM for grading the placental maturity of ultrasound images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as Li et al. [4]
An automatic method for objective assessment that complements the doctor's subjective assessment would provide a more precise interpretation for placental evaluation.
“Hence, an automatic method for the objective assessment that complements the doctor ' s subjective assessment would provide a more precise interpretation for placental evaluation [4, 5].”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [4]
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Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects
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Self-citationArtificial Intelligence in Prenatal Ultrasound Diagnosis
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“The automated classification of placental maturity was proposed by Lei et al. and Li et al. [116,117].”
BackgroundDiscriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector
Lei, B., Yao, Y., Chen, S., Li, S., Li, W., Ni, D., & Wang, T. (2015) · Scientific Reports · doi:10.1038/srep12818 · Cited by 28 · see citing works
“B-mode US imaging is one of the most frequently used US imaging especially for placental maturity staging [2-7].”
MethodSelf-citationMulti-modal and multi-layout discriminative learning for placental maturity staging
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Placental maturity evaluation via feature fusion and discriminative learning
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Self-citationHybrid descriptor for placental maturity grading
Lei, B., Jiang, F., Zhou, F., Ni, D., Yao, Y., Chen, S., & Wang, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08489-x · Cited by 3 · see citing works
Self-citationDilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound
Wu, Y., Yang, Y., Zhu, L., Han, Z., Luo, H., Xue, X., & Wang, W. (2023) · Frontiers in Physics · doi:10.3389/fphy.2023.1239400 · Cited by 2 · see citing works
Discriminative learning for automatic staging of placental maturity via multi-layer fisher vector
Li, W., Ni, D., Chen, S., Lei, B., Wang, T., Yao, Y., & Li, S. (2015) · International Conference on Information, Communications & Signal Processing (ICICS) · doi:10.1109/icics.2015.7459905 · Cited by 2 · see citing works
Self-citationPlacental maturity grading via hybrid descriptors based on fisher vector
Jiang, F., Ni, D., Chen, S., Yao, Y., Le, M. H., Wang, T., & Lei, B. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363861
Self-citation
- Authors
- Li, X., Yao, Y., Ni, D., Chen, S., Li, S., Lei, B., & Wang, T.
- Affiliations
- Shenzhen University (CN)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Venue
- Bio-Medical Materials and Engineering
- Publisher
- SAGE Publications
- Published
- 2014-11
- Volume
- 24
- Issue
- 6
- Pages
- 2821-2829
- ISSN
- 0959-2989 (ISSN-L)
- 1878-3619
- OpenAlex
- W1455359501
- Cited by
- 12
- References
- 10
- Citations by year
- 2025: 1 · 2023: 2 · 2021: 1 · 2020: 1 · 2018: 2 · 2016: 3 · 2015: 2
- FWCI
- 1.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bio-Medical Materials and Engineering (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- placental maturity, DAISY descriptor, B-mode ultrasound, scale and translation invariance
- MeSH terms
- Algorithms, Artificial Intelligence, Female, Gestational Age, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Placenta, Pregnancy, Sensitivity and Specificity, Reproducibility of Results, Ultrasonography, Prenatal
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