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Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this pape...
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| creator | Sobhaninia, Zahra Rafiei, Shima Emami, Ali Karimi, Nader Najarian, Kayvan Samavi, Shadrokh Reza Soroushmehr, S. M. |
| description | Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of pregnancy show that the segmentation results and the extracted HC match well with the radiologist annotations. The obtained dice scores of the fetal head segmentation and the accuracy of HC evaluations are comparable to the state-of-the-art. |
| doi_str_mv | 10.1109/EMBC.2019.8856981 |
| format | conference_proceeding |
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| fulltext | fulltext |
| identifier | EISSN: 1558-4615 |
| ispartof | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, Vol.2019, p.6545-6548 |
| issn | 1558-4615 2694-0604 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2341617772 |
| source | IEEE Xplore All Conference Series |
| subjects | Biomedical imaging Head Image segmentation Training Tuners Ultrasonic imaging |
| title | Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning |
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