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 for training and testing algorithms, which contained 1334 2D images from 551 women of the standard fetal head plane.
“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]
The Dataset section confirms that this study used the open-access HC18 dataset for training and testing and that its images came from 551 pregnant women. It reports 1,354 two-dimensional images, however, not the claimed 1,334. As one of five cited studies, this paper establishes its own use of HC18, not the other studies’ use. (LLM, conf=0.89)
Experimental — accuracy not yet measuredAI usage in standard plane acquisition has the benefit of application of a lightweight algorithm in point-of-care devices.
“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]
The Discussion (page unavailable) describes point-of-care use as a potential application and reports a 6.3 ms execution time on a computer. The Introduction (page unavailable) says the attention modules add few parameters at low computational cost. The paper does not report applying the algorithm on point-of-care devices or using it to acquire standard planes; it studies head segmentation and circumference measurement from existing images. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI models are 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.
“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]
The Discussion (page not provided) identifies point-of-care use and medically underserved areas as potential applications of automated fetal head-circumference measurement. It does not say the model improves ultrasound image acquisition or diagnostic accuracy in point-of-care devices; the dataset described in the Experiments section used images acquired by experienced sonographers. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAI models designed for automatic biometric measurements are usually restricted to a specific range of GA and can fail in images of different GA.
“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]
The Results and Discussion report that the model performed less well in the first trimester and had larger head-circumference errors in the third, supporting sensitivity to gestational stage. They do not say that AI models are usually restricted to a specific GA range or fail on images outside it; this model was tested across all three trimesters. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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BackgroundMethodInfluentialFetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
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- 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
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