Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning
Accurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and tends to be a time-intensive task. Furthermore, FMF...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/633 |
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| author | Zhonghua Liu Jin Wang Guorong Lyu Haisheng Song Weifeng Yu Peizhong Liu Yuling Fan Yaocheng Wan |
| author_facet | Zhonghua Liu Jin Wang Guorong Lyu Haisheng Song Weifeng Yu Peizhong Liu Yuling Fan Yaocheng Wan |
| author_sort | Zhonghua Liu |
| collection | DOAJ |
| description | Accurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and tends to be a time-intensive task. Furthermore, FMF angles are subjective when measured manually. To address this challenge, we propose a deep learning-based assisted examination framework for automatically measuring FMF angles on 2D ultrasound images. Firstly, we trained a deep learning network using 1549 fetal ultrasound images to achieve automatic and accurate segmentation of critical areas. Subsequently, a key point detection network was employed to predict the coordinates of the requisite points for calculating FMF angles. Finally, FMF angles were obtained through computational means. We employed Pearson correlation coefficients and Bland–Altman plots to assess the correlation and consistency between the model’s predictions and manual measurements. Notably, our method exhibited a mean absolute error of 2.354°, outperforming the typical standards of the junior expert. This indicates the high degree of accuracy and reliability achieved by our approach. |
| format | Article |
| id | doaj-art-4f8cfa0684514de3b4fdd71df0610c23 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4f8cfa0684514de3b4fdd71df0610c232025-08-20T02:12:29ZengMDPI AGSensors1424-82202025-01-0125363310.3390/s25030633Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep LearningZhonghua Liu0Jin Wang1Guorong Lyu2Haisheng Song3Weifeng Yu4Peizhong Liu5Yuling Fan6Yaocheng Wan7Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 350122, ChinaSchool of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, ChinaDepartment of Ultrasound, Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, ChinaSchool of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, ChinaDepartment of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 350122, ChinaSchool of Engineering, Huaqiao University, Quanzhou 362021, ChinaSchool of Engineering, Huaqiao University, Quanzhou 362021, ChinaSchool of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaAccurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and tends to be a time-intensive task. Furthermore, FMF angles are subjective when measured manually. To address this challenge, we propose a deep learning-based assisted examination framework for automatically measuring FMF angles on 2D ultrasound images. Firstly, we trained a deep learning network using 1549 fetal ultrasound images to achieve automatic and accurate segmentation of critical areas. Subsequently, a key point detection network was employed to predict the coordinates of the requisite points for calculating FMF angles. Finally, FMF angles were obtained through computational means. We employed Pearson correlation coefficients and Bland–Altman plots to assess the correlation and consistency between the model’s predictions and manual measurements. Notably, our method exhibited a mean absolute error of 2.354°, outperforming the typical standards of the junior expert. This indicates the high degree of accuracy and reliability achieved by our approach.https://www.mdpi.com/1424-8220/25/3/633automatic measurementdeep learningsemantic segmentationultrasound image |
| spellingShingle | Zhonghua Liu Jin Wang Guorong Lyu Haisheng Song Weifeng Yu Peizhong Liu Yuling Fan Yaocheng Wan Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning Sensors automatic measurement deep learning semantic segmentation ultrasound image |
| title | Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning |
| title_full | Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning |
| title_fullStr | Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning |
| title_full_unstemmed | Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning |
| title_short | Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning |
| title_sort | automatic measurement of frontomaxillary facial angle in fetal ultrasound images using deep learning |
| topic | automatic measurement deep learning semantic segmentation ultrasound image |
| url | https://www.mdpi.com/1424-8220/25/3/633 |
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