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CAD System Utilizing UNet and Hough Transform for Automated Measurement of Fetal Head Circumference and Age in 2D Ultrasound Images.

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Titel: CAD System Utilizing UNet and Hough Transform for Automated Measurement of Fetal Head Circumference and Age in 2D Ultrasound Images.
Autoren: Jaber, Hamzah, Mohammed, Ahmed Abed, Zhang, Bo, Qiu, Maidi, Zaid, Mustafa M. Abd, Sumari, Putra
Quelle: Artificial Intelligence & Applications (2811-0854); Jan2026, Vol. 4 Issue 1, p101-110, 10p
Schlagwörter: COMPUTER-aided diagnosis, HOUGH transforms, GESTATIONAL age, BIOMETRY, ULTRASONIC imaging, IMAGE segmentation, COMPUTER-assisted image analysis (Medicine), DEEP learning
Abstract: Two-dimensional (2D) medical ultrasound is a widely used imaging modality for the anatomical and functional assessment of fetal development due to its low cost, availability, real-time capability, and the absence of radiation hazards. Head circumference (HC) is an essential biometric to measure fetal growth. However, the low signal-to-noise ratio in ultrasound imaging can make it difficult for clinicians to identify the fetal plane correctly. Additionally, manually measuring HC can be expensive, involving accurately placing three minor and major parameter points from the ultrasound machine. To address these issues, research has been conducted to develop an automated system for measuring HC. This study presents a computer-aided diagnosis (CAD) system for the automatic measurement of fetal HC and fetal age using hybrid feature extraction. Using Convolutional Neural Networks (CNNs), self-supervised learning (SSL), vision transformers (ViTs), UNet deep learning model for segmentation, and Hough transform to measure performance, this study achieved higher performance compared to previous studies with a Dice similarity coefficient (DSC) of 97.23 ± 2.78, an average distance factor (ADF) of 2.8 ± 2.93 mm, a Jaccard Index of 88.57 ± 3.79, and an accuracy of 97.2%. After that, we enhance UNet using an attention mechanism that achieved a Dice coefficient of 98.5 ± 2.5, an ADF of 2.4 ± 2.8 mm, and an accuracy of 98.1%. This system provides a more cost-effective and accurate measurement of HC, aiding clinicians in assessing fetal development. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:Two-dimensional (2D) medical ultrasound is a widely used imaging modality for the anatomical and functional assessment of fetal development due to its low cost, availability, real-time capability, and the absence of radiation hazards. Head circumference (HC) is an essential biometric to measure fetal growth. However, the low signal-to-noise ratio in ultrasound imaging can make it difficult for clinicians to identify the fetal plane correctly. Additionally, manually measuring HC can be expensive, involving accurately placing three minor and major parameter points from the ultrasound machine. To address these issues, research has been conducted to develop an automated system for measuring HC. This study presents a computer-aided diagnosis (CAD) system for the automatic measurement of fetal HC and fetal age using hybrid feature extraction. Using Convolutional Neural Networks (CNNs), self-supervised learning (SSL), vision transformers (ViTs), UNet deep learning model for segmentation, and Hough transform to measure performance, this study achieved higher performance compared to previous studies with a Dice similarity coefficient (DSC) of 97.23 ± 2.78, an average distance factor (ADF) of 2.8 ± 2.93 mm, a Jaccard Index of 88.57 ± 3.79, and an accuracy of 97.2%. After that, we enhance UNet using an attention mechanism that achieved a Dice coefficient of 98.5 ± 2.5, an ADF of 2.4 ± 2.8 mm, and an accuracy of 98.1%. This system provides a more cost-effective and accurate measurement of HC, aiding clinicians in assessing fetal development. [ABSTRACT FROM AUTHOR]
ISSN:28110854
DOI:10.47852/bonviewAIA52025416