ArticleFrontiers in neurology2020
Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning.
Article in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review.Journal of clinical medicine · 2024Review
- Application of Deep Learning for Real-Time Ablation Zone Measurement in Ultrasound Imaging.Cancers · 2024Article
- FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery.IEEE open journal of engineering in medicine and biology · 2024Article
- Application and Progress of Artificial Intelligence in Fetal Ultrasound.Journal of clinical medicine · 2023Review
- Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach.Healthcare (Basel, Switzerland) · 2023Article
- Artificial intelligence in diagnostic ultrasonography.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Review
- Automatic Deep Learning-Based Pipeline for Automatic Delineation and Measurement of Fetal Brain Structures in Routine Mid-Trimester Ultrasound Images.Fetal diagnosis and therapy · 2023Article
- Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.Computational and mathematical methods in medicine · 2023Article
- Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images.iScience · 2022Article
- Review
- Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications.Frontiers in medicine · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measurement of the width of fetal lateral ventricles (LVs) in prenatal ultrasound (US) images is essential for antenatal neuronographic assessment. However, the manual measurement of LV width is highly subjective and relies on the clinical experience of scanners. To deal with this challenge, we propose a computer-aided detection framework for automatic measurement of fetal LVs in two-dimensional US images. First, we train a deep convolutional network on 2,400 images of LVs to perform pixel-wise segmentation. Then, the number of pixels per centimeter (PPC), a vital parameter for quantifying the caliper in US images, is obtained via morphological operations guided by prior knowledge. The estimated PPC, upon conversion to a physical length, is used to determine the diameter of the LV by employing the minimum enclosing rectangle method. Extensive experiments on a self-collected dataset demonstrate that the proposed method achieves superior performance over manual measurement, with a mean absolute measurement error of 1.8 mm. The proposed method is fully automatic and is shown to be capable of reducing measurement bias caused by improper US scanning.
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