TY - GEN
T1 - A comparative study on fetal head circumference measurement from ultrasound images using deep learning models
AU - Wang, Yu
AU - Yao, Yudong
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/12/16
Y1 - 2022/12/16
N2 - Ultrasound imaging is the most commonly used imaging modality for the prenatal examination of pregnant women, with real-Time imaging and no radiation characteristics. Through a ultrasound image of the fetal head, doctors can measure the fetal head circumference (HC) to evaluate fetal growth and potential delivery mode. In practice, fetal HC is usually measured manually by doctors based on ultrasound images. Manual measurement of fetal HC is subjective and time-consuming, which has a negative impact on measurement accuracy and efficiency. At present, deep learning is widely investigated in the medical field. Many researchers apply deep learning to measuring fetal HC to assist doctors to accurately and quickly completing the measurement of fetal HC. In this paper, we compare the performance of eight deep learning models (U-Net, Attention U-Net, GINet, global reasoning unit (GloRe), SegFormer, Segmenter, BiSeNet V2, and short-Term dense concatenate network (STDC)) on two fetal HC measurement datasets. SegFormer achieves the best results in Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute Difference (ADF). The performance of Attention U-Net is slightly worse than that of SegFormer.
AB - Ultrasound imaging is the most commonly used imaging modality for the prenatal examination of pregnant women, with real-Time imaging and no radiation characteristics. Through a ultrasound image of the fetal head, doctors can measure the fetal head circumference (HC) to evaluate fetal growth and potential delivery mode. In practice, fetal HC is usually measured manually by doctors based on ultrasound images. Manual measurement of fetal HC is subjective and time-consuming, which has a negative impact on measurement accuracy and efficiency. At present, deep learning is widely investigated in the medical field. Many researchers apply deep learning to measuring fetal HC to assist doctors to accurately and quickly completing the measurement of fetal HC. In this paper, we compare the performance of eight deep learning models (U-Net, Attention U-Net, GINet, global reasoning unit (GloRe), SegFormer, Segmenter, BiSeNet V2, and short-Term dense concatenate network (STDC)) on two fetal HC measurement datasets. SegFormer achieves the best results in Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute Difference (ADF). The performance of Attention U-Net is slightly worse than that of SegFormer.
KW - Deep Learning
KW - fetal head circumference
KW - head circumference measurement
UR - http://www.scopus.com/inward/record.url?scp=85158022497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85158022497&partnerID=8YFLogxK
U2 - 10.1145/3584376.3584613
DO - 10.1145/3584376.3584613
M3 - Conference contribution
AN - SCOPUS:85158022497
T3 - ACM International Conference Proceeding Series
SP - 1341
EP - 1348
BT - Proceedings of 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022
T2 - 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022
Y2 - 16 December 2022 through 18 December 2022
ER -