| 1 |
He F, Wang Y, Xiu Y, et al. Artificial intelligence in prenatal ultrasound diagnosis [J]. Front Med (Lausanne), 2021, 8: 729978.
|
| 2 |
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis [J]. Med Image Anal, 2017, 42: 60-88.
|
| 3 |
Zhang L, Chen S, Chin CT, et al. Intelligent scanning: automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination [J]. Med Phys, 2012, 39(8): 5015-5027.
|
| 4 |
Ryou H, Yaqub M, Cavallaro A, et al. Automated 3D ultrasound image analysis for first trimester assessment of fetal health [J]. Phys Med Biol, 2019, 64(18): 185010.
|
| 5 |
Smeets NA, Dvinskikh NA, Winkens B, et al. A new semi-automated method for fetal volume measurements with three-dimensional ultrasound: preliminary results [J]. Prenat Diagn, 2012, 32(8): 770-776.
|
| 6 |
Yang X, Yu L, Li S, et al. Towards Automated semantic segmentation in prenatal volumetric ultrasound [J]. IEEE Trans Med Imaging, 2019, 38(1): 180-193.
|
| 7 |
Bernardino F, Cardoso R, Montenegro N, et al. Semiautomated ultrasonographic measurement of fetal nuchal translucency using a computer software tool [J]. Ultrasound Med Biol, 1998, 24(1): 51-54.
|
| 8 |
Lee YB, Kim MJ, Kim MH. Robust border enhancement and detection for measurement of fetal nuchal translucency in ultrasound images [J]. Med Biol Eng Comput, 2007, 45(11): 1143-1152.
|
| 9 |
Deng Y, Wang Y, Chen P, et al. A hierarchical model for automatic nuchal translucency detection from ultrasound images [J]. Comput Biol Med, 2012, 42(6): 706-713.
|
| 10 |
Park J, Sofka M, Lee S, et al. Automatic nuchal translucency measurement from ultrasonography [J]. Med Image Comput Comput Assist Interv, 2013, 16(Pt 3): 243-250.
|
| 11 |
Anzalone A, Fusco G, Isgrò F, et al. A system for the automatic measurement of the nuchal translucency thickness from ultrasound video stream of the foetus [C]. IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, 20-22 June 2013.
|
| 12 |
Sciortino G, Orlandi E, Valenti C, et al. Wavelet analysis and neural network classifiers to detect mid-sagittal sections for nuchal translucency measurement [J]. Image Analysis & Stereology, 2016, 35(2): 105-115.
|
| 13 |
Sciortino G, Tegolo D, Valenti C. Automatic detection and measurement of nuchal translucency [J]. Comput Biol Med, 2017, 82: 12-20.
|
| 14 |
Nie S, Yu J, Chen P, et al. Automatic detection of standard sagittal plane in the first trimester of pregnancy using 3-D ultrasound data [J]. Ultrasound Med Biol, 2017, 43(1): 286-300.
|
| 15 |
Nie S, Yu J, Chen P, et al. Automatic measurement of fetal Nuchal translucency from three-dimensional ultrasound data [J]. Annu Int Conf IEEE Eng Med Biol Soc, 2017, 2017: 3417-3420.
|
| 16 |
Dudley NJ, Chapman E. The importance of quality management in fetal measurement [J]. Ultrasound Obstet Gynecol, 2002, 19(2): 190-196.
|
| 17 |
van Nisselrooij AEL, Teunissen AKK, Clur SA, et al. Why are congenital heart defects being missed? [J]. Ultrasound Obstet Gynecol, 2020, 55(6): 747-757.
|
| 18 |
Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis [J]. Lancet Digit Health, 2019, 1(6): e271-e297.
|
| 19 |
Chen H, Ni D, Qin J, et al. Standard plane localization in fetal ultrasound via domain transferred deep neural networks [J]. IEEE J Biomed Health Inform, 2015, 19(5): 1627-1636.
|
| 20 |
Chen H, Dou Q, Ni D, et al. Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks [J]. Medical Image Computing And Cmoputer-Assisted Intervention, 2015, 9349: 504-514.
|
| 21 |
Lei B, Tan EL, Chen S, et al. Automatic recognition of fetal facial standard plane in ultrasound image via fisher vector [J]. PLoS One, 2015, 10(5): e0121838.
|
| 22 |
Yu Z, Tan EL, Ni D, et al. A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition [J]. IEEE J Biomed Health Inform, 2018, 22(3): 874-885.
|
| 23 |
Burgos-Artizzu XP, Coronado-Gutiérrez D, Valenzuela-Alcaraz B, et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes [J]. Sci Rep, 2020, 10(1): 10200.
|
| 24 |
Cai Y, Droste R, Sharma H, et al. Spatio-temporal visual attention modelling of standard biometry plane-finding navigation [J]. Med Image Anal, 2020, 65: 101762.
|
| 25 |
Baumgartner CF, Kamnitsas K, Matthew J, et al. SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound [J]. IEEE Trans Med Imaging, 2017, 36(11): 2204-2215.
|
| 26 |
Sinclair M, Baumgartner CF, Matthew J, et al. Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks [J]. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 714-717.
|
| 27 |
Zeng Y, Tsui PH, Wu W, et al. Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-net [J]. J Digit Imaging, 2021, 34(1): 134-148.
|
| 28 |
Kim B, Kim KC, Park Y, et al. Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images [J]. Physiol Meas, 2018, 39(10): 105007.
|
| 29 |
Luo D, Wen H, Peng G, et al. A prenatal ultrasound scanning approach: one-touch technique in second and third trimesters [J]. Ultrasound Med Biol, 2021,47(8):2258-2265.
|
| 30 |
Chen X, He M, Dan T, et al. Automatic measurements of fetal lateral ventricles in 2D ultrasound images using deep learning [J]. Front Neurol, 2020, 11: 526.
|
| 31 |
Pluym ID, Afshar Y, Holliman K, et al. Accuracy of automated three-dimensional ultrasound imaging technique for fetal head biometry [J]. Ultrasound Obstet Gynecol, 2021, 57(5): 798-803.
|
| 32 |
Cho HC, Sun S, Min Hyun C, et al. Automated ultrasound assessment of amniotic fluid index using deep learning [J]. Med Image Anal, 2021, 69: 101951.
|
| 33 |
Yang X, Wang X, Wang Y, et al. Hybrid attention for automatic segmentation of whole fetal head in prenatal ultrasound volumes [J]. Comput Methods Programs Biomed, 2020, 194: 105519.
|
| 34 |
Sahli H, Mouelhi A, Ben Slama A, et al. Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images [J]. J Med Eng Technol, 2019, 43(5): 279-286.
|
| 35 |
Burgos-Artizzu XP, Coronado-Gutiérrez D, Valenzuela-Alcaraz B, et al. Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age [J]. Am J Obstet Gynecol MFM, 2021, 3(6): 100462.
|
| 36 |
Yeo L, Romero R. Fetal Intelligent Navigation Echocardiography (FINE): a novel method for rapid, simple, and automatic examination of the fetal heart [J]. Ultrasound Obstet Gynecol, 2013, 42(3): 268-284.
|
| 37 |
Yeo L, Markush D, Romero R. Prenatal diagnosis of tetralogy of fallot with pulmonary atresia using: Fetal Intelligent Navigation Echocardiography (FINE) [J]. J Matern Fetal Neonatal Med, 2019, 32(21): 3699-3702.
|
| 38 |
Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease [J]. Nat Med, 2021, 27(5): 882-891.
|
| 39 |
Cobo T, Bonet-Carne E, Martínez-Terrón M, et al. Feasibility and reproducibility of fetal lung texture analysis by Automatic Quantitative Ultrasound Analysis and correlation with gestational age [J]. Fetal Diagn Ther, 2012, 31(4): 230-236.
|
| 40 |
Chen P, Chen Y, Deng Y, et al. A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images [J]. Int J Comput Assist Radiol Surg, 2020, 15(8): 1407-1415.
|
| 41 |
Palacio M, Cobo T, Martínez-Terrón M, et al. Performance of an automatic quantitative ultrasound analysis of the fetal lung to predict fetal lung maturity [J]. Am J Obstet Gynecol, 2012, 207(6): 504.e1-504.e5045.
|
| 42 |
Burgos-Artizzu XP, Perez-Moreno Á, Coronado-Gutierrez D, et al. Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis [J]. Sci Rep, 2019, 9(1): 1950.
|
| 43 |
Baños N, Perez-Moreno A, Migliorelli F, et al. Quantitative analysis of the cervical texture by ultrasound and correlation with gestational age [J]. Fetal Diagn Ther, 2017, 41(4): 265-272.
|
| 44 |
Baños N, Perez-Moreno A, Julià C, et al. Quantitative analysis of cervical texture by ultrasound in mid-pregnancy and association with spontaneous preterm birth [J]. Ultrasound Obstet Gynecol, 2018, 51(5): 637-643.
|
| 45 |
Lei B, Yao Y, Chen S, et al. Discriminative learning for automatic staging of placental maturity via multi-layer fisher vector [J]. Sci Rep, 2015, 5: 12818.
|
| 46 |
Gupta K, Balyan K, Lamba B, et al. Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy [J]. J Matern Fetal Neonatal Med, 2021, 35(25): 5587-5594.
|
| 47 |
Wu L, Cheng JZ, Li S, et al. FUIQA: fetal ultrasound image quality assessment with deep convolutional networks [J]. IEEE Trans Cybern, 2017, 47(5): 1336-1349.
|
| 48 |
Lin Z, Li S, Ni D, et al. Multi-task learning for quality assessment of fetal head ultrasound images [J]. Med Image Anal, 2019, 58: 101548.
|
| 49 |
Dong J, Liu S, Liao Y, et al. A generic quality control framework for fetal ultrasound cardiac four-chamber planes [J]. IEEE J Biomed Health Inform, 2020, 24(4): 931-942.
|
| 50 |
谭莹, 文华轩, 彭桂艳, 等. 在线产科超声图像智能质量控制系统的临床应用价值 [J/OL]. 中华医学超声杂志(电子版), 2022, 19(7): 649-655.
|