KaydedilenlerDerginizi ekleyin

Dergiler / Acta Infologica / 2022 / Volume: 6 Issue: 2

Ultrason Görüntülerinden Fetus Sağlığının Bilgisayar Destekli Takibi: Bir İnceleme

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Deniz Atas(CANAKKALE ONSEKIZ MART UNIVERSITY)0000-0003-0427-1257
Yonca Bayrakdar Yılmaz(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0001-8708-1752
Sayfa
—
Yayın Tarihi
2022-12-31
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Son olarak, fetüs sağlığının bilgisayar destekli izlenmesi üzerine güncel tekniklerle ilgili ana zorluklar ve araştırmaların gelecekteki yönü özetlenmiştir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bilgisayar destekli tanı yöntemleri, tıp uzmanlarına yıllardır fetüs sağlığını izlemek için yardımcı olmaktadır.
📊
Temel Bulgular & Veri:Yeni yöntemlerin kullanılması, fetüsün sağlığının izlenmesinin yanı sıra anomalilerin tanısında da olumlu bir etki yaratmıştır.
💡
Sonuç & Katkı:Son olarak, fetüs sağlığının bilgisayar destekli izlenmesi üzerine güncel tekniklerle ilgili ana zorluklar ve araştırmaların gelecekteki yönü özetlenmiştir.
🔬Tür:Review
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Computer-Aided Diagnosis · Fetus Health · Deep Learning

Özet

Bilgisayar destekli tanı yöntemleri, tıp uzmanlarına yıllardır fetüs sağlığını izlemek için yardımcı olmaktadır. Yeni yöntemlerin kullanılması, fetüsün sağlığının izlenmesinin yanı sıra anomalilerin tanısında da olumlu bir etki yaratmıştır. Bu çalışma ilk olarak fetüste anomalileri tanımlamak için kullanılan göstergeleri tanıtır ve geleneksel görüntü işleme, makine öğrenimi ve derin öğrenme gibi bilgisayar tabanlı yöntemler hakkında temel bilgiler verir. Daha sonra, ultrason görüntülerinden fetüs sağlığının izlenmesi ve anomali tespitinde yeni teknikler kullanan mevcut çalışmalara genel bir bakış verilmiştir. Son olarak, fetüs sağlığının bilgisayar destekli izlenmesi üzerine güncel tekniklerle ilgili ana zorluklar ve araştırmaların gelecekteki yönü özetlenmiştir.

Abstract

Computer aided diagnostic methods have been helping medical experts for monitoring fetus health for many years. The use of new methods has made a positive effect in monitoring the health of fetus as well as the diagnosis of anomalies. This study first introduces the indicators for identifying anomalies in fetus and gives basic information about computer-based methods such as traditional image processing, machine learning and deep learning. Then an overview of existing studies which use novel techniques on monitoring fetus health and anomaly detection from ultrasound images is given. Finally, the main challenges of novel techniques and future directions of research on computer-aided monitoring of fetus health are summarized.

🔖

Bu Makaleye Atıf Yap

Atas, D., Yılmaz, Y. B. (2022). Ultrason Görüntülerinden Fetus Sağlığının Bilgisayar Destekli Takibi: Bir İnceleme. Acta Infologica, Volume: 6 Issue: 2. https://doi.org/10.26650/acin.1099106

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (105)

  1. Abdallah, Y., Daemen, A., Kirk, E., Pexsters, A., Naji, O., Stalder, J. Bourne, T. (2012). Limitations of Current Definitions of Miscarriage Using Mean Gestational Sac Diameter and Crown-Rump Length Measurements. Obstetrical & Gynecological Survey, 67(3), 146-147. https://doi.org/10.1002/ uog.10109 google scholar
  2. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S. Asari, V. K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8(3), 292. https://doi.org/10.3390/electronics8030292 google scholar
  3. Amoah, B., Anto, E. A., & Crimi, A. (2015). Automatic fetal measurements for low-cost settings by using Local Phase Bone detection, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (pp. 161-164). google scholar
  4. Anjit, T. A., & Rishidas, S. (2011). Identification of nasal bone for the early detection of down syndrome using Back Propagation Neural Network. International Conference on Communications and Signal Processing (pp.136-140). google scholar
  5. Anzalone, A., Fusco, G., Isgro, F., Orlandi, E., Prevete, R., Sciortino, G. Valenti, C. (2013). A system for the automatic measurement of the nuchal translucency thickness from ultrasound video stream of the foetus. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems (pp. 239-244). google scholar
  6. Aydoğdu, A., Aydoğdu, Y., & Yakıncı, Z. (2017). Temel Radyolojik İnceleme Yöntemlerini Tanıma [Recognition Of Basic Radiological Investigation Methods]. İnönü Üni. Sağlık Hizmetleri MYO Dergisi, 5(2), 44-53. google scholar
  7. Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir A. E. (2018). Machine learning for medical ultrasound: status, methods, and future opportunities. Abdominal Radiology, 43(4), 786-799. https://doi.org/10.1007/s00261-018-1517-0 google scholar
  8. Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008). Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree. IEEE Transactions on Medical Imaging, 27 (9), 1342-1355. https://doi.org/10.1109/tmi.2008.928917. google scholar
  9. Chakkarwar, V. A., Joshi, M. S., & Revankar, P. S. (2010). Automated analysis of gestational sac in medical image processing. In: IEEE 2nd International Advance Computing Conference (IACC) (pp. 304-309). google scholar
  10. Chaoui, R., Benoit, B., Mitkowska-Wozniak, H., Heling, K. S., & Nicolaides, K. H. (2009). Assessment of intracranial translucency (IT) in the detection of spina bifida at the 11-13-week scan. Ultrasound in Obstetrics and Gynecology, 34(3), 249-252. https://doi.org/10.1002/uog.7329. google scholar
  11. Chaudhari, K., & Oza, S. (2021). Ultrasound image based fully-automated nuchal translucency segmentation and thickness measurement. International Journal of Nonlinear Analysis and Applications, 12(Special Issue), 1573-1583. https://doi.org/10.22075/IJNAA.2021.5830. google scholar
  12. Chen, X., He, M., Dan, T., Wang, N., Lin, M., Zhang, L. Xie, H. (2020). Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning. Frontiers in Neurology, 11, 526. https://doi.org/10.3389/fneur.2020.00526. google scholar
  13. Contreras Ortiz, S. H., Chiua, T., & Fox, M. D. (2012). Ultrasound image enhancement: A review. Biomedical Signal Processing and Control, 7, 419-428. https://doi.org/10.1016/j.bspc.2012.02.002. google scholar
  14. D’Addario, V. (2015). Fetal mild ventriculomegaly: still a challenging problem. Journal of Perinatal Medicine, 43(1), 5-9. https://doi.org/10.1515/ jpm-2014-0280. google scholar
  15. Da Silva, E. A. B. & Mendonça, G. V. (2005). Digital Image Processing, The Electrical Engineering Handbook, 891-910. Elsevier Academic Press google scholar
  16. Deng, Y., Wang, Y., Chen, P., & Yu, J. (2012). A hierarchical model for automatic nuchal translucency detection from ultrasound images. Computers in Biology and Medicine, 42(6), 706-713. https://doi.org/10.1016/j.compbiomed.2012.04.002. google scholar
  17. Ergün, E. (2017). Birinci Trimester Ultrasonografi İncelemesi [First Trimester Ultrasonography Examination], Trd Sem, 5, 185-201. google scholar
  18. Fiorentino, M. C., Moccia, S., Capparuccini, M., Giamberini, S., & Frontoni, E. (2020). A regression framework to head circumference delineation from US fetal images. Computer Methods and Programs in Biomedicine, 198, 105771. https://doi.org/10.1016/j.cmpb.2020.105771. google scholar
  19. Foi, A., Maggioni, M., Pepe, A., Rueda, S., Noble, J. A., Papageorghiou, A.T., & Tohka, J. (2014). Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation. Computerized Medical Imaging and Graphics, 38 (8), 774-784. https://doi.org/10.1016/j.compmedimag.2014.09.006. google scholar
  20. Ghahramani, Z. (2004). Unsupervised Learning. In: Bousquet, O., von Luxburg, U., Ratsch, G. (Eds), Advanced Lectures on Machine Learning, ML 2003, Lecture Notes in Computer Science (pp. 72-112). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-540-28650-9_5 google scholar
  21. Gonzalez, R. C. & Woods, R. E. (2018). Digital Image Processing Fourth Edition. UK: Pearson. google scholar
  22. Hermawati, F. A., Tjandrasa, H., Sugiono, Sari, P., & Azis, A. (2019). Automatic femur length measurement for fetal ultrasound image using localizing region-based active contour method. IOP Conf. Series: Journal of Physics: Conf. Series, 1230, 012002. https://doi. org/10.1088/1742-6596/1230/1/012002 google scholar
  23. Hiremath, P. S., Prema, T., & Badiger, S. (2013). Speckle Noise Reduction in Medical Ultrasound Images. In: Gunarathne, G. (Ed.), Advancements and Breakthroughs in Ultrasound Imaging. IntechOpen. https://doi.org/10.5772/56519 google scholar
  24. Hurwitz, J. & Kirsch, D. (2018). Machine Learning For Dummies. IBM Limited Edition. google scholar
  25. Ibrahim, D. A., Al-Assam, H., Jassim, S., & Du, H. (2017). Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images. Medical Image Understanding and Analysis (pp. 86-97). Switzerland: Springer International Publishing. google scholar
  26. Jang, J., Park, Y., Kim, B., Lee, S. M., Kwon, J., Seo, J. K. (2018). Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images, IEEE Journal of Biomedical and Health Informatics, 22 (5), 1512-1520. https://doi.org/10.1109/jbhi.2017.2776116 google scholar
  27. Jiao, L., & Zhao, J. (2019). A Survey on the New Generation of Deep Learning in Image Processing. IEEE Access, 7, 172231-172263. https://doi.org/10.1109/ access.2019.2956508. google scholar
  28. Johnson, A., (2018). ISUOG Basic Training Fetal Biometry - Dating, Assessing Size & Estimating Fetal Weight Trish Chudleigh, UK, ISUOG’s Basic Training Programme in Singapore google scholar
  29. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research, 4, 237-285. https://doi.org/10.48550/arXiv.cs/9605103 google scholar
  30. Karl, K., Kagan, K. O., & Chaoui, R. (2012). Intra- and interoperator reliability of manual and semi-automated measurements of intracranial translucency. Ultrasound in Obstetrics & Gynecology, 39(2), 164-168. https://doi.org/10.1002/uog.10137 google scholar
  31. Khan, N. H., Tegnander, E., Dreier, J. M., Eik-Nes. S., Torp, H., & Kiss, G. (2014). Automatic measurement of biparietal diameter with a portable ultrasound device. In: IEEE International Ultrasonics Symposium (pp. 459-462). google scholar
  32. Khan, N. H., Tegnander, E., Dreier, J. M., Eik-Nes, S., & Torp, H. (2015). Automatic detection and measurement of fetal femur length using a portable ultrasound device. IEEE International Ultrasonics Symposium (IUS) (pp. 1-4). google scholar
  33. Khazendar, S., Farren, J., Al-Assam, H., Sayasneh, A., Du, H., Bourne, T., & Jassim, S. A. (2014). Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image. Mobile Multimedia/Image Processing, Security and Applications. https://doi. org/10.1117/12.2057720. google scholar
  34. Kim, H. P., Lee, S. M., Kwon, J., Park, Y., Kim, K. C., & Seo, J. K. (2019). Automatic evaluation of fetal head biometry from ultrasound images using machine learning. Physiological Measurement, 40(6), 065009. https://doi.org/10.1088/1361-6579/ab21ac. google scholar
  35. Krizhevsky, A., Sutskever, I., & Hinton G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386. google scholar
  36. Lachmann, R., Picciarelli, G., Moratalla, J., Greene, N., & Nicolaides, K. H. (2010). Frontomaxillary facial angle in fetuses with spina bifida at 11-13 weeks’ gestation. Ultrasound in Obstetrics and Gynecology, 36(3), 268-271. https://doi.org/10.1002/uog.7718. google scholar
  37. Lakshmi, P., Geetha, M., Menon, N. R., Krishnan, V., & Nedungadi, P. (2008). Automated Screening for Trisomy 21 by measuring Nuchal Translucency and Frontomaxillary Facial Angle. International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1738-1743). google scholar
  38. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521: 436-444. https://doi.org/10.1038/nature14539. google scholar
  39. Li, J., Wang, Y., Lei, B., Cheng, J., Qin, J., Wang, T. Ni, D. (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. https://doi.org/10.1109/JBHI.2017.2703890. google scholar
  40. Li, P., Zhao, H., Liu, P., & Cao, F. (2020). Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images. Medical & Biological Engineering & Computing, 58(11), 2879-2892. https://doi.org/10.1007/s11517-020- 29 02242-5. google scholar
  41. Liu, T., Xu, M., Zhang, Z., Dai, C., Wang, H., Zhang, R. Wu, S. (2019). Direct Detection and Measurement of Nuchal Translucency with Neural Networks from Ultrasound Images. Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis (PIPPI, SUSI) (pp. 20-28). google scholar
  42. Lu, W., Tan, J., & Floyd, R. (2005). Automated fetal head detection and measurement in ultrasound images by iterative randomized hough transform. Ultrasound in Medicine & Biology, 31(7): 929-936. https://doi.org/10.1016/j.ultrasmedbio.2005.04.002. google scholar
  43. Mathews, M., Deepa, J., Tonu, J., & Shari, T. (2014). Segmentation of Head from Ultrasound Fetal Image using Chamfer Matching and Hough Transform based Approaches. International Journal of Engineering Research & Technology (IJERT), 3(5), 1232-1235. google scholar
  44. Mathews, M., & Deepa, J. (2014). Segmentation of Femur from Ultrasound Fetal Image using Shape based Approach. International Journal of Modern Communication Technologies & Research (IJMCTR), 2 (6), 2321-0850. google scholar
  45. Mathiesen, J. M., Aksglaede, L., Skibsted, L., Petersen, O. B., & Tabor, A. (2014). Outcome of fetuses with short femur length detected at second-trimester anomaly scan: a national survey. Ultrasound in Obstetrics & Gynecology, 44(2). 160-165. https://doi.org/10.1002/uog.13286. google scholar
  46. Mounica, S., Ramakrishnan, S., & Thamotharan, B. (2019). A Study on Preprocessing Techniques for Ultrasound Images of Carotid Artery. Lecture Notes in Computational Vision and Biomechanics (pp. 1725-1738). google scholar
  47. Mukherjee, P., Swamy, G., Gupta, M., Patil, U., & Krishnan, K. B. (2010). Automatic detection and measurement of femur length from fetal ultrasonography. Medical Imaging 2010: Ultrasonic Imaging, Tomography, and Therapy. 762909. google scholar
  48. Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons, 4, 51-62. google scholar
  49. Nie, S., Yu, J., Chen, P., Wang, Y., Guo, Y., & Zhang, J. Q. (2017). Automatic measurement of fetal Nuchal translucency from three dimensional ultrasound data. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3417-3420). google scholar
  50. Nirmala, S., & Palanisamy, V. (2010). Clinical decision support system for early prediction of Down syndrome fetus using sonogram images. Signal, Image and Video Processing, 5 (2), 245-255. https://doi.org/10.1007/s11760-010-0158-8 google scholar
  51. Oghli, M.G., Shabanzadeh, A., Moradi, S., Sirjani, N., Gerami, R., Ghaderi, P.,., Zaidi, H. (2021). Automatic fetal biometry prediction using a novel deep convolutional network architecture. Physica Medica, 88, 127-137. https://doi.org/10.1016/j.ejmp.2021.06.020. google scholar
  52. Paladini, D. & Volpe, P. (2014). Ultrasound of Congenital Fetal Anomalies Differential Diagnosis and Prognostic Indicators (2nd ed.). London, UK: CRC Press. google scholar
  53. Park, J., Sofka, M., Lee, S., Kim, D., & Zhou, S. K. (2013). Automatic Nuchal Translucency Measurement from Ultrasonography. Medical Image Computing and Computer-Assisted Intervention — MICCAI, Springer, Berlin, Heidelberg (pp. 243-250). google scholar
  54. Rafeek, T., & Gunasundari, A. (2013). Reliable non-invasive first trimester screening test using image processing and artificial neural network. International Journal of Engineering Research and Applications, 3(3), 662-669. google scholar
  55. Rasheed, K., Junejo, F., Malik, A., & Saqib, M. (2021). Automated Fetal Head Classification and Segmentation Using Ultrasound Video. IEEE Access, 9,160249-160267. https://doi.org/10.1109/ACCESS.2021.3131518. google scholar
  56. Ravishankar, H., Prabhu, S. M., & Vaidya, V. (2016). Singhal N. Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning. IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 779-782). google scholar
  57. Rawat, V., Jain, A., Shrimali, V., & Rawat, A. (2013). Automatic assessment of foetal biometric parameter using GVF snakes. International Journal of Biomedical Engineering and Technology, 12(4), 321-333. https://doi.org/10.1155/2018/6452050. google scholar
  58. Razzak, M. I., Naz, S., & Zaib, A. (2017). Deep Learning for Medical Image Processing: Overview, Challenges and the Future. Classification in BioApps. Switzerland: Springer International Press (pp. 323-350). google scholar
  59. Rueda, S., Fathima, S., Knight, C.L., Yaqub, M., Papageorghiou, A.T., Rahmatullah, B.,., Noble, J.A. (2014). Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge. IEEE Transactions on Medical Imaging, 33(4), 797-813. https://doi.org/10.1109/TMI.2013.2276943. google scholar
  60. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S.,., Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115, 211-252. https://doi.org/10.48550/arXiv.1409.0575. google scholar
  61. Sahli, H., Mouelhi, A., Ben Slama, A., Sayadi, M., & Rachdi, R. (2019). Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images. Journal of Medical Engineering & Technology, 43(5), 279-286. https://doi.org/10.1080/03091902.2019. 1653389 google scholar
  62. Salomon, L. J., Alfirevic, Z., Da Silva Costa, F., Deter, R. L., Figueras, F., Ghi, T.,., Yeo, G. (2019). ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound in Obstetrics & Gynecology, 53(6), 715-723. https://doi.org/10.1002/uog.20272. google scholar
  63. Samuel, A. L. (1959), Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), 210-229. google scholar
  64. Satwika, I. P., Rahmatullah, R., Habibie, I., Nurhadiyatna, A., & Jatmiko, W. (2013). Improved e cient ellipse hough transform for fetal head measurement. In: International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 375-379) google scholar
  65. Sciortino, G., Tegolo, D., & Valenti, C. (2017). A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks. XXVI International Conference on Information, Communication and Automation Technologies (ICAT) (pp. 1-7). google scholar
  66. Serhatlıoğlu, S. (2016). Tıbbi Bilişim ve Bilgisayar Destekli Tanı [Medical Informatics and Computer Aided Diagnosis]. F.Ü.Sağ.Bil.Tıp Derg., 30 (1), 39- 41. google scholar
  67. Sinclair, M., Baumgartner, C. F., Matthew, J., Bai, W., & Martinez, J. C. (2018). Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 714-717). google scholar
  68. Snijders, R. J. M., & Nicolaides, K. H. (1994). Fetal biometry at 14-40 weeks’ gestation. Ultrasound in Obstetrics and Gynecology, 4(1), 34-48. google scholar
  69. Sobhaninia, Z., Rafiei, S., Emami, A., Karimi, N., Najarian, K., Samavi, S., & Soroushmehr, S. M. R. (2019). Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6545-6548). google scholar
  70. Sofka, M., Zhang, J., Good, S., Zhou, S. K., & Comaniciu, D. (2014). Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN). IEEE Transactions on Medical Imaging, 33(5), 1054-1070. google scholar
  71. Sonia, R., & Shanti, V. (2015). Early detection of down syndrome marker using fetal nasal bone length during first and second trimester. Indian Journal Applied Research, 5(7), 152-157. google scholar
  72. Sonia, R., & Shanthi, V. (2016). Early Detection of Down Syndrome Marker by Measuring Fetal Nuchal Translucency Thickness from Ultrasound Images during First Trimester. Indian Journal of Science and Technology, 9 (21), 1-6. google scholar
  73. Thirusittampalam, K., & Thangavel, J. (2020). Fetal Head Detection in 2D Ultrasound Images using Deep Learning. International Conference on Advances in Computing and Technology (ICACT-2020) Proceedings (pp. 71-72). google scholar
  74. Thomas, J. G., Peters, R. A., & Jeanty, P. (1991). Automatic segmentation of ultrasound images using morphological operators. IEEE Transactions on Medical Imaging, 10(2), 180-186. google scholar
  75. Thomas, M. & Arjunan, S. (2022). Deep Learning Measurement Model to Segment the Nuchal Translucency Region for the Early Identification of Down Syndrome. Measurement Science Review, 22(4), 187-192. https://doi.org/10.2478/msr-2022-0023. google scholar
  76. Van den Heuvel, T. L. A., de Bruijn, D., de Korte, C. L., & van Ginneken, B. (2018). Automated measurement of fetal head circumference using 2D ultrasound images. PLOS ONE, 13(8), e0200412. https://doi.org/10.1371/journal.pone.0200412. google scholar
  77. Vincett, P. S., & Sahyun, M. R. V. (2003). Photographic Processes and Materials. Encyclopedia of Physical Science and Technology. google scholar
  78. Wang, C. (2014). Automatic entropy-based femur segmentation and fast length measurement for fetal ultrasound images. In: International Conference on Advanced Robotics and Intelligent Systems (ARIS) (pp. 1-5). google scholar
  79. Wang, W., Qin, J., Zhu, L., Ni, D., & Chui, Y.-P. (2014). Detection and Measurement of Fetal Abdominal Contour in Ultrasound Images via Local Phase Information and Iterative Randomized Hough Transform. Bio-Medical Materials and Engineering, 24, 1261-1267. google scholar
  80. Wang, X., Yang, X., Dou, H., Li, S., & Heng, P. (2019). Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp.1-5). google scholar
  81. Wee, L. K., Arooj, A., & Supriyanto, E. (2010). Computerized Automatic Nasal Bone Detection based on Ultrasound Fetal Images Using Cross Correlation Techniques. WSEAS Transactions on Information Science and Applications, 7(8), 1068-1077. google scholar
  82. Y ang, X., Yu, L., Li, S., Wen, H., Luo, D., Bian, C.,., Heng, P. (2019). Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound. IEEE Transactions on Medical Imaging, 38(1), 180-193. https://doi.org/10.1109/TMI.2018.2858779. google scholar
  83. Y ang, Y., Yang, P., & Zhang, B. (2020). Automatic segmentation in fetal ultrasound images based on improved U-net. Journal of Physics: Conference Series, CISAI, 1693, 012183. https://doi.org/10.1088/1742-6596/1693/1/012183. google scholar
  84. Y u, J., Wang, Y., Chen, P., & Shen, Y. (2008). Fetal Abdominal Contour Extraction and Measurement in Ultrasound Images. Ultrasound in Medicine & Biology, 34 (2), 169-182. https://doi.org/10.1016/j.ultrasmedbio.2007.06.026. google scholar
  85. Yu, J., Wang, Y., & Chen, P. (2015). Automatic US Image Analysis for Fetal Femur Length Measurement Based on Phase Symmetry and Saliency Visual Attention Model. Journal of Medical Imaging and Health Informatics, 5(7), 1474-1481. https://doi.org/10.1166/jmihi.2015.1571. google scholar
  86. Zhang, L., Chen, S., Li, S., & Wang T. (2011). Automatic measurement of early gestational sac diameters from one scan session. Medical Imaging: Computer-Aided Diagnosis, 7963, 42-49. https://doi.org/10.1117/12.881343. google scholar
  87. Zhang, L., Chen, S., Chin, C. T., Wang, T., & Li, S. (2012). Intelligent scanning: Automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination. Medical Physics, 39(8), 5015-5027. https://doi.org/10.1118/1.4736415. google scholar
  88. Zhang, L., Dudley, N. J., Lambrou, T., Allinson, N., & Ye, X. (2017). Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image. Journal of Medical Imaging, 4(2), 024001. https://doi.org/10.1117/1.jmi.4.2.024001. google scholar
  89. Zhang, J., Petitjean, C., Lopez, P., & Ainouz, S. (2020). Direct estimation of fetal head circumference from ultrasound images based on regression CNN. Proceedings of Machine Learning Research, 121, 914-922. google scholar
  90. Zhang, J., Petitjean, C., & Ainouz, S. (2022), Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images. Journal of Imaging, 8(2), 23. https://doi.org/10.3390/jimaging8020023. google scholar
  91. Zhen, L., Yang, X., Ting, Y. H., Chen, M., & Leung, T. Y. (2013). The inuence of image setting on intracranial translucency measurement by manual and semi-automated system. Prenatal Diagnosis 33(9), 889-893. google scholar
  92. Zhou, Z. H. (2009). Ensemble Learning. Encyclopedia of Biometrics (pp. 270-273). Berlin: Springer. google scholar
  93. Zhu, X. (2007). Semi-supervised learning literature survey. Department of Computer Sciences, University of Wisconsin-Madison: Madison, WI, USA. google scholar
  94. Zhu, F., Liu, M., Wang, F., Qui, D., Li, R., & Dai, C. (2021). Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet[J]. Mathematical Biosciences and Engineering, 18(6): 7790-7805. https://doi.org/10.3934/mbe.2021387. google scholar
  95. Abdallah, Y., Daemen, A., Kirk, E., Pexsters, A., Naji, O., Stalder, J. Bourne, T. (2012). Limitations of Current Definitions of Miscarriage Using Mean Gestational Sac Diameter and Crown-Rump Length Measurements. Obstetrical & Gynecological Survey, 67(3), 146-147. https://doi.org/10.1002/ uog.10109 google scholar
  96. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S. Asari, V. K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8(3), 292. https://doi.org/10.3390/electronics8030292 google scholar
  97. Anzalone, A., Fusco, G., Isgro, F., Orlandi, E., Prevete, R., Sciortino, G. Valenti, C. (2013). A system for the automatic measurement of the nuchal translucency thickness from ultrasound video stream of the foetus. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems (pp. 239-244). google scholar
  98. Chen, X., He, M., Dan, T., Wang, N., Lin, M., Zhang, L. Xie, H. (2020). Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning. Frontiers in Neurology, 11, 526. https://doi.org/10.3389/fneur.2020.00526. google scholar
  99. Li, J., Wang, Y., Lei, B., Cheng, J., Qin, J., Wang, T. Ni, D. (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. https://doi.org/10.1109/JBHI.2017.2703890. google scholar
  100. Liu, T., Xu, M., Zhang, Z., Dai, C., Wang, H., Zhang, R. Wu, S. (2019). Direct Detection and Measurement of Nuchal Translucency with Neural Networks from Ultrasound Images. Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis (PIPPI, SUSI) (pp. 20-28). google scholar
  101. Oghli, M.G., Shabanzadeh, A., Moradi, S., Sirjani, N., Gerami, R., Ghaderi, P.,., Zaidi, H. (2021). Automatic fetal biometry prediction using a novel deep convolutional network architecture. Physica Medica, 88, 127-137. https://doi.org/10.1016/j.ejmp.2021.06.020. google scholar
  102. Rueda, S., Fathima, S., Knight, C.L., Yaqub, M., Papageorghiou, A.T., Rahmatullah, B.,., Noble, J.A. (2014). Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge. IEEE Transactions on Medical Imaging, 33(4), 797-813. https://doi.org/10.1109/TMI.2013.2276943. google scholar
  103. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S.,., Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115, 211-252. https://doi.org/10.48550/arXiv.1409.0575. google scholar
  104. Salomon, L. J., Alfirevic, Z., Da Silva Costa, F., Deter, R. L., Figueras, F., Ghi, T.,., Yeo, G. (2019). ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound in Obstetrics & Gynecology, 53(6), 715-723. https://doi.org/10.1002/uog.20272. google scholar
  105. Y ang, X., Yu, L., Li, S., Wen, H., Luo, D., Bian, C.,., Heng, P. (2019). Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound. IEEE Transactions on Medical Imaging, 38(1), 180-193. https://doi.org/10.1109/TMI.2018.2858779. google scholar