Abstract
Early in pregnancy, ultrasounds are used to confirm the fetal heartbeat and a uterine pregnancy. Later, ultrasounds screen for fetal growth, placenta location and umbilical cord, as well as the baby's general health and anatomy. Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it is equally difficult for a non-expert to identify relevant structures within the image. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. The proposed method is realized with deep convolutional neural network models to find the region of interest (ROI) of the fetal biometric and organs region in the US image. Based on the ROI, AlexNet, GoogleNet and CNN evaluate the image quality by assessing the goodness of depiction for the key structures of fetal biometrics. In this method both normal and abnormal US data are considered. In addition with that the input sources of the neural network are augmented with the local phase features along with the original US data. These augmented input sources helps to improve the performance of the various Neural Networks. The input sources are trained by AlexNet, GoogleNet and CNN. Then the process of validation is done by performance in proposed Networks for evaluating the accuracy. The performance of proposed work is evaluated with different network configuration. On the dataset of 400 images used in this classification task, proposed work of AlexNet, GoogleNet and CNN achieves accuracy of 90.43%, 88.70%, and 81.25% with reference to expert’s ground truth results respectively.
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Aditya, Y. N., Abduljabbar, H. N., Pahl, C., Wee, L. K., & Supriyanto, E. (2013). Fetal weight and gender estimation using computer based ultrasound images analysis. International Journal of Computers, 7(1), 12–21.
Anjit, T. A., & Rishidas, S. (2011). Identification of nasal bone for the early detection of down syndrome using back propagation neural network. In 2011 International conference on communications and signal processing (pp. 136–140) Calicut, India
Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., et al. (2017). SonoNet: real-time detection and localization of fetal standard scan planes in freehand ultrasound. IEEE Transactions On Medical Imaging, 36(11), 2204–2214.
Bindiya, H. M., Chethana, H. T., & Pavan Kumar, S. P. (2018). Detection of anomalies in fetus using convolutional neural network. International Journal of Information Technology and Computer Science (IJITCS), 10(11), 77–86.
Chuang, L., Hwang, J.-Y., Chang, C.-H., Yu, C.-H., & Chang, F.-M. (2002). Ultrasound estimation of fetal weight with the use of computerized artificial neural network model. Ultrasound in Medicine and Biology, 28(8), 991–996.
Coakley, F. V., Glenn, O. A., Qayyum, A., Barkovich, A. J., Goldstein, R., & Filly, R. A. (2004). Fetal MR imaging: A developing modality for the developing patient. American Journal of Roentgenology, 182, 243–252.
Farmer, R. M., Medearis, A. L., Hirata, G. I., & Platt, L. D. (1992). The use of a neural network for the ultrasonography estimation of fetal weight in the macrosomicfetus. American Journal of Obstetrics & Gynecology, 166(5), 1467–1472.
Fiorentinoa, M. C., Moccia, S., Capparuccinia, M., Giamberinia, S., & Frontonia, E. (2020). A regression framework to head-circumference delineation from US fetal images. Computer Methods and Programs in Biomedicine, 198, 105771.
Garel, C., Chantrel, E., Brisse, H., Elmaleh, M., Luton, D., Oury, J. F., et al. (2001). Fetalcerebral cortex: normal gestational landmarks identified using PrenatalMR imaging. American Journal of Neuroradiology, 22, 184–189.
Girard, N., Raybaud, C., Gambarelli, D., & Figarellabranger, D. (2001). Fetal brain MR imaging. MRI Clinics of North America, 9, 19–56.
Glenn, O. A. (2006). Fetal central nervous system MR imaging. Neuroimaging Clinics of North America, 16, 1–17.
Gurgen, F., Onal, E., & Varol, F. G. (1997). IUGR detection by ultrasonography examinations using neural networks. IEEE Engineering in Medicine and Biology Magazine, 16(3), 55–58.
Huppi, P. S., & Inder, T. E. (2001). Magnetic resonance techniques in the evaluation of the perinatal brain: recent advances and future directions. Seminars In Neonatology, 6, 195–210.
Khashman, A., & Curtis, K. M. (1996). Neural networks arbitration for automatic edge detection of 3-dimensional objects. In Proceedings of third international conference on electronics, circuits, and systems (pp. 49–52). Rodos, Greece.
Khashman, A., & Curtis, K. M. (1997). Automatic edge detection of fetal head and abdominal circumferences using neural network arbitration. In Proceedings of the IEEE international symposium on industrial electronics, 1997. ISIE '97 (Vol. 3, pp. 1191–1194). Guimaraes, Portugal.
Kim, B., Kim, K. C., Park, Y., Kwon, J.-Y., Jang, J., & Seo, J. K. (2018). Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images. In PMEA-102688.R1, Physiological Measurement, 2018, Institute of Physics and Engineering in Medicine (pp. 1–22).
Levine, D. (2004). Fetal magnetic resonance imaging. Journal of Maternal Fetal and Neonatal Medicine, 15, 85–94.
Li, J., Wang, Y., Lei, B., Cheng, J.-Z., Qin, J., Wang, T., et al. (2018). Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE Journal of Biomedical and Health Informatics, 22(1), 215–223.
Prayer, D., Brugger, P. C., & Prayer, L. (2004). Fetal MRI: techniques and protocols. Pediatric Radiology, 34, 685–693.
Ramya, R., Srinivasan, K., Pavithra Devi, K., Preethi, S., & Poonkuzhali, G. (2018). Prenatal fetal weight detection using image processing. International Journal of Scientific & Technology Research, 7(8), 37–39.
Rawat, V., Jain, A., & Shrimali, V. (2016). Automatic detection of fetal abnormality using head and abdominal circumference. In International conference on computational collective intelligence, vol. 9876 of lecture notes in computer science (pp. 525–534), Springer, Cham, Switzerland.
Rydberg, C., & Tunon, K. (2017). Detection of fetal abnormalities by second-trimester ultrasound screening in a non-selected population. ActaObstetricia et GynecologiaScandinavica, 96, 176–182.
Sobhaninia, Z., Rafiei, S., Emami, A., Karimi, N., Najarian, K., Samavi, S., & Reza Soroushmehr, S. M. (2019). Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. In 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 6545–6548).
Sonigo, P. C., Rypens, F. F., Carteret, M., & Delezoide, A. (1998). MR imaging of fetal cerebral anomalies. Pediatric Radiology, 28, 212–222.
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Selvathi, D., Chandralekha, R. Fetal biometric based abnormality detection during prenatal development using deep learning techniques. Multidim Syst Sign Process 33, 1–15 (2022). https://doi.org/10.1007/s11045-021-00765-0
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DOI: https://doi.org/10.1007/s11045-021-00765-0