ArticleJournal of digital imaging2021
Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 107 citations in OpenAlex.
- Measurement-Oriented Evaluation of Deep Learning Models for Automated Fetal Head Biometry in Prenatal Ultrasound.Journal of clinical medicine · 2026Article
- Freehand 3D ultrasound imaging of the femoral head and neck.International journal of computer assisted radiology and surgery · 2026Article
- Dual-channel grouped cross-dimension attention V-Net for pulmonary nodule segmentation.Quantitative imaging in medicine and surgery · 2025Article
- The performance of sonographic antenatal birth weight assessment assisted with artificial intelligence compared to that of manual examiners at term.Archives of gynecology and obstetrics · 2025Article
- Deep learning using nasal endoscopy and T2-weighted MRI for prediction of sinonasal inverted papilloma-associated squamous cell carcinoma: an exploratory study.European radiology experimental · 2025Article
- Partial dependence of ultrasonically estimated fetal weight on biometric parameters.Royal Society open science · 2025Article
- Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence.BMC medical imaging · 2025Article
- Ensemble fuzzy deep learning for brain tumor detection.Scientific reports · 2025Article
- Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.Journal of imaging informatics in medicine · 2025Article
- Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review.Journal of clinical medicine · 2024Review
- Fetal Ultrasound Segmentation and Measurements Using Appearance and Shape Prior Based Density Regression with Deep CNN and Robust Ellipse Fitting.Journal of imaging informatics in medicine · 2024Article
- FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery.IEEE open journal of engineering in medicine and biology · 2024Article
- Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning.Nature communications · 2023Article
- Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.Biomimetics (Basel, Switzerland) · 2023Review
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- A Clinical Approach to Semiautomated Three-Dimensional Fetal Brain Biometry-Comparing the Strengths and Weaknesses of Two Diagnostic Tools: 5DCNS+Journal of clinical medicine · 2023Article
- A combined deformable model and medical transformer algorithm for medical image segmentation.Medical & biological engineering & computing · 2023Article
- Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction.Diagnostics (Basel, Switzerland) · 2022Article
- Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images.iScience · 2022Article
- BIoMT-ISeg: Blockchain internet of medical things for intelligent segmentation.Frontiers in physiology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.