A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow

Akkus, 2017, Deep learning for brain MRI segmentation: state of the art and future directions, J Digit Imaging, 30, 449, 10.1007/s10278-017-9983-4 Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371 Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput, 18, 1527, 10.1162/neco.2006.18.7.1527 Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 1097 LeCun, 1989, Backpropagation applied to handwritten ZIP code recognition, Neural Comput, 1, 541, 10.1162/neco.1989.1.4.541 Deng Russakovsky, 2015, ImageNet large scale visual recognition challenge, Int J Comput Vis, 115, 211, 10.1007/s11263-015-0816-y Lin, 2016, Neural networks for computer-aided diagnosis in medicine: a review, Neurocomputing, 216, 700, 10.1016/j.neucom.2016.08.039 Akkus Z, Kostandy P, Philbrick AK, Erickson BJ. Robust brain extraction tool for CT head images. Neurocomputing. In press. Akkus Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005 Milletari Weston, 2018, Automated abdominal segmentation of CT scans for body composition analysis using deep learning, Radiology, 290, 669, 10.1148/radiol.2018181432 Kooi, 2017, Large scale deep learning for computer aided detection of mammographic lesions, Med Image Anal, 35, 303, 10.1016/j.media.2016.07.007 Cheng, 2016, Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans, Sci Rep, 6, 24454, 10.1038/srep24454 Akkus Chi, 2017, Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network, J Digit Imaging, 30, 477, 10.1007/s10278-017-9997-y Brattain, 2018, Machine learning for medical ultrasound: status, methods, and future opportunities, Abdom Radiol (NY), 43, 786, 10.1007/s00261-018-1517-0 Litjens, 2016, Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Sci Rep, 6, 26286, 10.1038/srep26286 Janowczyk, 2016, Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases, J Pathol Inform, 7, 29, 10.4103/2153-3539.186902 He Choi, 2017, A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment, Thyroid, 27, 546, 10.1089/thy.2016.0372 Burman, 2015, Thyroid nodules, N Engl J Med, 373, 2347, 10.1056/NEJMcp1415786 Jemal, 2005, Cancer statistics 2005, CA Cancer J Clin, 55, 10, 10.3322/canjclin.55.1.10 Hegedüs, 2004, The thyroid nodule, N Engl J Med, 351, 1764, 10.1056/NEJMcp031436 Cooper, 2009, Revised American Thyroid Association management guidelines for patients with thyroid nodules and differentiated thyroid cancer, Thyroid, 19, 1167, 10.1089/thy.2009.0110 Frates, 2006, Management of thyroid nodules detected at US: Society of Radiologists in Ultrasound consensus conference statement, Ultrasound Q, 22, 231, 10.1097/01.ruq.0000226877.19937.a1 Guille, 2015, Evaluation and management of the pediatric thyroid nodule, Oncologist, 20, 19, 10.1634/theoncologist.2014-0115 Ma, 2017, A pre-trained convolutional neural network based method for thyroid nodule diagnosis, Ultrasonics, 73, 221, 10.1016/j.ultras.2016.09.011 Ma, 2017, Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images, Med Phys, 44, 1678, 10.1002/mp.12134 Li, 2018, An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images, Sci Rep, 8, 6600, 10.1038/s41598-018-25005-7 Girshick Li, 2019, Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study, Lancet Oncol, 20, 193, 10.1016/S1470-2045(18)30762-9 He Redmon Szegedy Pereira Shikhman, 2018 Schwab, 2016, Inter- and Intra-observer agreement in ultrasound BI-RADS classification and real-time elastography Tsukuba score assessment of breast lesions, Ultrasound Med Biol, 42, 2622, 10.1016/j.ultrasmedbio.2016.06.017 Grimm, 2015, Interobserver variability between breast imagers using the fifth edition of the BI-RADS MRI lexicon, AJR Am J Roentgenol, 204, 1120, 10.2214/AJR.14.13047 Byra, 2019, Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Med Phys, 46, 746, 10.1002/mp.13361 Simonyan Han, 2017, A deep learning framework for supporting the classification of breast lesions in ultrasound images, Phys Med Biol, 62, 7714, 10.1088/1361-6560/aa82ec Zhang, 2016, Deep learning based classification of breast tumors with shear-wave elastography, Ultrasonics, 72, 150, 10.1016/j.ultras.2016.08.004 Yap, 2018, Automated breast ultrasound lesions detection using convolutional neural networks, IEEE J Biomed Health Inform, 22, 1218, 10.1109/JBHI.2017.2731873 Kumar, 2018, Automated and real-time segmentation of suspicious breast masses using convolutional neural network, PLoS ONE, 13, 10.1371/journal.pone.0195816 Ronneberger, 2015, U-Net: convolutional networks for biomedical image segmentation, vol 9351, 234 Wang, 2019, Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study, Gut, 68, 729, 10.1136/gutjnl-2018-316204 Meng, 2017, Liver fibrosis classification based on transfer learning and FCNet for ultrasound images, IEEE Access, 5, 5804 Liu, 2017, Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification, Sensors, 17, 149, 10.3390/s17010149 Wu, 2014, Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound, Optik, 125, 4057, 10.1016/j.ijleo.2014.01.114 Biswas, 2018, Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm, Comput Methods Programs Biomed, 155, 165, 10.1016/j.cmpb.2017.12.016 Byra, 2018, Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images, Int J Comput Assist Radiol Surg, 13, 1895, 10.1007/s11548-018-1843-2 Yu, 2018, A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition, IEEE J Biomed Health Inform, 22, 874, 10.1109/JBHI.2017.2705031 Wu, 2017, FUIQA: fetal ultrasound image quality assessment with deep convolutional networks, IEEE Trans Cybern, 47, 1336, 10.1109/TCYB.2017.2671898 Chen, 2017, Ultrasound standard plane detection using a composite neural network framework, IEEE Trans Cybern, 47, 1576, 10.1109/TCYB.2017.2685080 Menchón-Lara Lekadir, 2017, A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound, IEEE J Biomed Health Inform, 21, 48, 10.1109/JBHI.2016.2631401 Hetherington, 2017, SLIDE: automatic spine level identification system using a deep convolutional neural network, Int J Comput Assist Radiol Surg, 12, 1189, 10.1007/s11548-017-1575-8 Cheng, 2017, Transfer learning with convolutional neural networks for classification of abdominal ultrasound images, J Digit Imaging, 30, 234, 10.1007/s10278-016-9929-2 Nair Luchies, 2018, Deep neural networks for ultrasound beamforming, IEEE Trans Med Imaging, 37, 2010, 10.1109/TMI.2018.2809641 Perdios Yoon, 2018, Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning, IEEE Trans Med Imaging. Wu, 2018, Direct reconstruction of ultrasound elastography using an end-to-end deep neural network, vol 11070, 374 Zeiler Zeiler Zhou Zeiler, 2010 Springenberg Zhou Chattopadhay Li, 2016, Visual saliency detection based on multiscale deep CNN features, IEEE Trans Image Process, 25, 5012, 10.1109/TIP.2016.2602079 Philbrick, 2018, What does deep learning see? Insights from a classifier trained to predict contrast enhancement phase from CT images, AJR Am J Roentgenol, 211, 1184, 10.2214/AJR.18.20331 He Szegedy Badrinarayanan, 2017, SegNet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans Pattern Anal Mach Intell, 39, 2481, 10.1109/TPAMI.2016.2644615