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Attention mechanism optimized neural network for automatic measurement of fetal anterior-neck-lower-jaw angle in nuchal translucency tests

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Abstract

Nuchal translucency (NT) examination has become a standard item in early pregnancy tests because of its clinical value in detecting early gestational fetal abnormalities. Meanwhile, practitioners may sometimes have difficulty meeting the required criteria for NT measurement owing to their complexity or the exorbitant demand for NT tests. Thus, NT image quality control is critical, particularly for the frequently neglected criterion that the fetal head posture (FHP) shall be neither hyperextended nor hyperflexionated. The Nuchal Translucency Quality Review Program (NTQR) defines all FHPs quantitatively using the anterior-neck-lower-jaw angle (ANLJA), namely the angle between the fetal anterior neck and its lower jaw. According to NTQR’s definitions, hyperflexion is defined as ANLJA close to 0∘; hyperextension, ANLJA greater than 90∘; and normal FHP, ANLJA between 0∘ and 90∘. Focusing on FHP classification, we proposed a novel algorithm combining an attention mechanism and a convolutional neural network to predict ANLJAs. The new approach integrated channel and spatial attention and was capable of swiftly locating regions of interest. It abstracted important image features based on the information weight and attenuated useless information. The manual method was traditionally regarded as the gold standard for ANLJA measurement. Our findings indicated that the proposed algorithm performed admirably in the ANLJA prediction. It surpassed all other comparison algorithms in terms of efficiency.

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Acknowledgements

A small part of the flowcharts were created by Biorender.com. We are grateful to Ms. Wenjuan Li and Ms. Yimin Liao for their contribution to this study.

Funding

This work is funded by the National Natural Science Foundation of China (under Grant No. 61772180), the Major Scientic and Technological Projects for collaborative prevention and control of birth defects in Hunan Province (No. 2019SK1010), the Science and Technology Innovation Projects of Hunan Province (No.2018SK50504), the Scientic Research Project of Hunan Provincial Commission (No.B202309026062 and B2019030), and the Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ70008).

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Authors and Affiliations

  1. Department of Ultrasonography, the Second Xiangya Hospital of Central South University, NO.139 Renmin Middle Road, Changsha, 410028, Hunan, China

    Yu-lin Peng, Shi Zeng & Long-mei Yao

  2. Department of Ultrasonography, the Hunan Provincial Maternal and Child Health Care Hospital, NO.53 Xiangchun Road, Changsha, 410008, Hunan, China

    Yu-lin Peng & Ying-chun Luo

  3. School of Computer Science, Hubei University of Technology, NO.28 Nanli Road, Wuhan, 430068, Hubei, China

    Ling-yu Yan

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Correspondence to Shi Zeng.

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Peng, Yl., Zeng, S., Luo, Yc. et al. Attention mechanism optimized neural network for automatic measurement of fetal anterior-neck-lower-jaw angle in nuchal translucency tests. Multimed Tools Appl 83, 15629–15648 (2024). https://doi.org/10.1007/s11042-023-15491-x

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