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Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net
Open accessAbstract
Automatic computerized segmentation of fetal head from ultrasound images and head circumference (HC) biometric measurement is still challenging, due to the inherent characteristics of fetal ultrasound images at different semesters of pregnancy. In this paper, we proposed a new deep learning method for automatic fetal ultrasound image segmentation and HC biometry: deeply supervised attention-gated (DAG) V-Net, which incorporated the attention mechanism and deep supervision strategy into V-Net models. In addition, multi-scale loss function was introduced for deep supervision. The training set of the HC18 Challenge was expanded with data augmentation to train the DAG V-Net deep learning models. The trained models were used to automatically segment fetal head from two-dimensional ultrasound images, followed by morphological processing, edge detection, and ellipse fitting. The fitted ellipses were then used for HC biometric measurement. The proposed DAG V-Net method was evaluated on the testing set of HC18 (n = 355), in terms of four performance indices: Dice similarity coefficient (DSC), Hausdorff distance (HD), HC difference (DF), and HC absolute difference (ADF). Experimental results showed that DAG V-Net had a DSC of 97.93%, a DF of 0.09 ± 2.45 mm, an AD of 1.77 ± 1.69 mm, and an HD of 1.29 ± 0.79 mm. The proposed DAG V-Net method ranks fifth among the participants in the HC18 Challenge. By incorporating the attention mechanism and deep supervision, the proposed method yielded better segmentation performance than conventional U-Net and V-Net methods. Compared with published state-of-the-art methods, the proposed DAG V-Net had better or comparable segmentation performance. The proposed DAG V-Net may be used as a new method for fetal ultrasound image segmentation and HC biometry. The code of DAG V-Net will be made available publicly on https://github.com/xiaojinmao-code/ .
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“Five studies used the freely available ‘HC18’ data set [45] for training and testing the algorithms, which contained 1334 2D images from 551 women of the standard fetal head plane [18,34,37,38,40].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“the application of a lightweight algorithm in point-of-care devices [18,34,37]”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI models are additionally able to enhance image acquisition and diagnostic accuracy in point-of-care US devices, which is of particular significance for application in low-resource settings and medically underserved areas [34,145,148,199].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI models designed for automatic biometric measurements are usually restraint to a specific range of GA and can fail in images of different GA [34,40,78].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [34]
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Metadata
- Authors
- Zeng, Y., Tsui, P.-H., Wu, W., Zhou, Z., & Wu, S.
- Affiliations
- Beijing University of Technology (CN)
- Chang Gung University (TW)
- Capital Medical University (CN)
- Corresponding author
- Zhou, Zhuhuang
- Wu, Shuicai
- Venue
- Journal of Digital Imaging
- Published
- 2021-01-22
- Volume
- 34
- Issue
- 1
- Pages
- 134-148
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC7887128
- OpenAlex
- W3124211545
- Cited by
- 113
- References
- 46
- Citations by year
- 2026: 17 · 2025: 30 · 2024: 15 · 2023: 24 · 2022: 20 · 2021: 7
- FWCI
- 5.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (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
- fetal head segmentation, deep supervision, attention mechanism, data augmentation, ellipse fitting, fetal biometry
- MeSH terms
- Biometry, Female, Head, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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