Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment

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• 关键词：3D ultrasound ; First ; trimester scan ; Random Forests ; Convolutional Neural Networks ; Fetal plane localization
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10019
• 期：1
• 页码：196-204
• 全文大小：2,537 KB
• 参考文献：1.Salomon, L., et al.: ISUOG practice guidelines: performance of first-trimester fetal ultrasound scan. Ultrasound Obstet. Gynecol. 41(1), 102–113 (2013)MathSciNet CrossRef
  2.Abu-Rustum, R.S., Daou, L., Abu-Rustum, S.E.: Role of first-trimester sonography in the diagnosis of aneuploidy and structural fetal anomalies. J. Ultrasound Med. 29(10), 1445–1452 (2010)
  3.Bartlett, L.A., et al.: Risk factors for legal induced abortion–related mortality in the United States. Obstet. Gynecol. 103(4), 729–737 (2004)CrossRef
  4.Dyson, R., et al.: Three-dimensional ultrasound in the evaluation of fetal anomalies. Ultrasound Obstet. Gynecol. 16(4), 321–328 (2000)CrossRef
  5.Chen, H., et al.: Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J. Biomed. Health Inform. 19(5), 1627–1636 (2015)CrossRef
  6.Maraci, M.A., Napolitano, R., Papageorghiou, A., Noble, J.: Searching for structures of interest in an ultrasound video sequence. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 133–140. Springer, Heidelberg (2014)
  7.Rahmatullah, B., Papageorghiou, A.T., Noble, J.: Integration of local and global features for anatomical object detection in ultrasound. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 402–409. Springer, Heidelberg (2012)CrossRef
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  9.Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014)
  10.Dollár, P., Zitnick, C.: Structured forests for fast edge detection. In: Proceedings of the IEEE International Conference on Computer Vision (2013)
  11.Yaqub, M., et al.: Investigation of the role of feature selection and weighted voting in random forests for 3-D volumetric segmentation. IEEE Trans. Med. Imaging 33(2), 258–271 (2014)CrossRef
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  13.Gao, Y., Maraci, M.A., Noble, J.A.: Describing ultrasound video content using deep convolutional neural networks. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (2016)
  14.Papageorghiou, A.T., et al.: International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project. Lancet 384(9946), 869–879 (2014)CrossRef
  
• 作者单位：Hosuk Ryou (18)
  Mohammad Yaqub (18)
  Angelo Cavallaro (19)
  Fenella Roseman (19)
  Aris Papageorghiou (19)
  J. Alison Noble (18)
  
  18. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
  19. Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, UK
  
• 丛书名：Machine Learning in Medical Imaging
• ISBN：978-3-319-47157-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10019

文摘

In this paper, we present a fully automated machine-learning based solution to localize the fetus and extract the best fetal biometry planes for the head and abdomen from 11–13+6days week 3D fetal ultrasound (US) images. Our method to localize the whole fetus in the sagittal plane utilizes Structured Random Forests (SRFs) and classical Random Forests (RFs). A transfer learning Convolutional Neural Network (CNNs) is then applied to axial images to localize one of three classes (head, body and non-fetal). Finally, the best fetal head and abdomen planes are automatically extracted based on clinical knowledge of the position of the fetal biometry planes within the head and body. Our hybrid method achieves promising localization of the best biometry fetal planes with 1.6 mm and 3.4 mm for head and abdomen plane localization respectively compared to the best manually chosen biometry planes.