A Systematic Review on the Role of Artificial Intelligence in Sonographic Diagnosis of Thyroid Cancer: Past, Present and Future
-
Fatemeh Abdolali
,
Atefeh Shahroudnejad
,
Sepideh Amiri
,
Abhilash Rakkunedeth Hareendranathan
,
Jacob L Jaremko
,
Michelle Noga
,
Kumaradevan Punithakumar
Abstract
Thyroid cancer is common worldwide with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration to determine whether the nodule is malignant. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems aimed at characterizing thyroid nodules based on ultrasound scans. Earlier approaches along these lines relied on clinically relevant features that were manually identified by radiologists. With the recent success of Artificial Intelligence (AI), various new methods using deep learning are being developed to identify these features in thyroid ultrasound automatically. In this paper, we present a systematic review of state-of-the-art on Artificial Intelligence (AI) application in sonographic diagnosis of thyroid cancer. This review follows a methodology-based classification of the different techniques available for thyroid cancer diagnosis, from methods using feature-based models to the most recent deep learning-based approaches. In this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis. Machine learning will continue to play a fundamental role in the development of future thyroid cancer diagnosis frameworks.
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3- Topstad D, Dickinson JA “Thyroid cancer incidence in Canada: a national cancer registry analysis”. C open 5:E612, 2017.
4- Danaei M, Haghdoost A, Momeni M. “An Epidemiological Review of Common Cancers in Iran; A Review Article”. Iranian Journal of Blood and Cancer 11(3):77-84, 2019.
5- Iran Cancer Statistics 2018 World Health Organization, https://gco.iarc.fr/today/data/factsheets/populations/364- iran-islamic-republic-of-fact-sheets.pdf 6- Anil G, Hegde A, Chong FV “Thyroid nodules: risk stratification for malignancy with ultrasound and guided biopsy”. Cancer Imaging, 11: 209, 2011.
7- Grant EG, Tessler FN, Hoang JK, Langer JE, Beland MD, Berland LL, Cronan JJ, Desser TS, Frates MC, Hamper UM, others “Thyroid ultrasound reporting lexicon: white paper of the ACR thyroid imaging, reporting and data system (TIRADS) committee”. J Am Coll Radiol, 12:1272–1279, 2015.
8- Li J, Ma X, Cui K Re:“ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI RADS Committee.” J Am Coll Radiol 15:380–381, 2018.
9- Acharya UR, Swapna G, Sree SV, Molinari F, Gupta S, Bardales RH, Witkowska A, Suri JS “A review on ultrasound-based thyroid cancer tissue characterization and automated classification”. Technol Cancer Res Treat 13:289–301, 2014.
10- Khachnaoui H, Guetari R, Khlifa N “A review on Deep Learning in thyroid ultrasound Computer-Assisted Diagnosis systems”. IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France, December 12-14, 291-297, 2018.
11- Sollini M, Cozzi L, Chiti A, Kirienko M “Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand?”. Eur J Radiol, 99:1–8, 2018.
12- Verburg F, Reiners C “Sonographic diagnosis of thyroid cancer with support of AI”. Nature Reviews Endocrinology, 15:1, 2019.
13- Zhang B, Tian J, Pei S, Chen Y, He X, Dong Y, Zhang L, Mo X, Huang W, Cong S, others “Machine Learning-- Assisted System for Thyroid Nodule Diagnosis”. Thyroid 29: 858-867, 2019.
14- Xia J, Chen H, Li Q, Zhou M, Chen L, Cai Z, Fang Y, Zhou H “Ultrasound-based differentiation of malignant and benign thyroid nodules: an extreme learning machine approach”. Comput Methods Programs Biomed, 147:37– 49, 2017.
15- Patricio M, Oliveira C, Caseiro-Alves F “Differentiating malignant thyroid nodule with statistical classifiers based on demographic and ultrasound features”, IEEE 5th Portuguese Meeting on Bioengineering (ENBENG), Coimbra, Portugal, February 16- 18, 1-4, 2017.
16- Illanes A, Esmaeili N, Poudel P, Balakrishnan S, Friebe M “Parametrical modelling for texture characterization— A novel approach applied to ultrasound thyroid segmentation”. PLoS One 14:e0211215, 2019.
17- China D, Illanes A, Poudel P, Friebe M, Mitra P, Sheet D “Anatomical structure segmentation in ultrasound volumes using cross frame belief propagating iterative random walks”. IEEE J Biomed Heal informatics 23: 1110- 1118, 2018.
18- Koundal D, Gupta S, Singh S “Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set”. Appl Soft Comput 40:86–97, 2016.
19- Narayan NS, Marziliano P, Kanagalingam J, Hobbs CGL “Speckle patch similarity for echogenicity-based multiorgan segmentation in ultrasound images of the thyroid gland”. IEEE J Biomed Heal informatics 21:172– 183, 2015.
20- Chang C-Y, Lei Y-F, Tseng C-H, Shih S-R “Thyroid segmentation and volume estimation in ultrasound images”. IEEE Trans Biomed Eng 57:1348–1357, 2010.
21- Prochazka A, Gulati S, Holinka S, Smutek D “Patch based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition”. Comput Med Imaging Graph, 71:9–18, 2019.
22- Yu Q, Jiang T, Zhou A, Zhang L, Zhang C, Xu P “Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images”. Eur Arch Oto-rhino-laryngology, 274:2891–2897, 2017. 23- “ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform”. Comput Methods Programs Biomed 107:233–241, 2012.
24- Tsantis S, Dimitropoulos N, Cavouras D, Nikiforidis G “Morphological and wavelet features towards sonographic thyroid nodules evaluation”. Comput Med Imaging Graph, 33:91–99, 2009.
25- Ding J, Cheng HD, Huang J, Zhang Y, Liu J “An improved quantitative measurement for thyroid cancer detection based on elastography”. Eur J Radiol, 81:800– 805, 2012.
26- Acharya UR, Chowriappa P, Fujita H, Bhat S, Dua S, Koh JEW, Eugene LWJ, Kongmebhol P, Ng K-H “Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images”. Knowledge-Based Syst, 107:235– 245, 2016.
27- Chang Y, Paul AK, Kim N, Baek JH, Choi YJ, Ha EJ, Lee KD, Lee HS, Shin D, Kim N “Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments”. Med Phys 43:554–567, 2016.
28- Chang C-Y, Chen S-J, Tsai M-F “Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images”. Pattern Recognit, 43:3494–3506, 2010.
29- Koundal D, Gupta S, Singh S “Computer aided thyroid nodule detection system using medical ultrasound images”. Biomed Signal Process Control, 40:117–130, 2018.
30- Ouyang F, Guo B, Ouyang L, Liu Z, Lin S, Meng W, Huang X, Chen H, Qiu-gen H, Yang S “Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules”. Eur J Radiol, 113:251–257, 2019.
31- Poudel P, Illanes A, Sheet D, Friebe M “Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches”. J Healthc Eng, 1-13, 2018.
32- Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O “3D U-Net: learning dense volumetric segmentation from sparse annotation”, International Conference on Medical Image Computing and Computer Assisted Intervention, Athens, Greece, October 17-21, 424-432, 2016.
33- Wunderling T, Golla B, Poudel P, Arens C, Friebe M, Hansen C “Comparison of thyroid segmentation techniques for 3D ultrasound Medical Imaging”. Image Processing: 10133: 1013317, 2017.
34- Zhou S, Wu H, Gong J, Le T, Wu H, Chen Q, Xu Z “Mark-Guided Segmentation of Ultrasonic Thyroid Nodules Using Deep Learning Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine”. Chengdu, China, October 13-15, 21- 26, 2018.
35- Ronneberger O, Fischer P, Brox T “U-net: Convolutional networks for biomedical image segmentation International Conference on Medical image computing and computer-assisted intervention”. Munich, Germany, October 5-9, 234-241, 2015.
36- Ma J, Wu F, Zhu J, Kong D, others “Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images”. Med Phys 44:1678–1691, 2017.
37- Li X, Wang S, Wei X, Zhu J, Yu R, Zhao M, Yu M, Liu Z, Liu S “Fully Convolutional Networks for Ultrasound Image Segmentation of Thyroid Nodules IEEE 20th International Conference on High Performance Computing and Communications”; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, June 28-30, 886- 890, 2018.
38- Ren S, He K, Girshick R, Sun J “Faster r-cnn: Towards real-time object detection with region proposal networks Advances in neural information processing systems”. Montreal, Quebec, Canada, December 7-12, 91-99, 2015.
39- Li H, Weng J, Shi Y, Gu W, Mao Y, Wang Y, Liu W, Zhang J “An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images”. Sci Rep 8: 1-12, 2018.
40- Redmon J, Farhadi A “YOLO9000: better, faster, stronger” Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, United States, July 21-26, 7263- 7271, 2017.
41- Szegedy C, Ioffe S, Vanhoucke V, Alemi AA “Inception-v4, inception-resnet and the impact of residual connections on learning”, Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, February 4–9, 4278- 4284, 2017.
42- Wang L, Yang S, Yang S, Zhao C, Tian G, Gao Y, Chen Y, Lu Y “Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network”. World J Surg Oncol 17:12, 2019.
43- Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, Xin X, Qin C, Wang X, Li J, others “Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study”. Lancet Oncol 20:193–201, 2019.
44- He K, Zhang X, Ren S, Sun J “Deep residual learning for image recognition”, Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, June 27-30, 770- 778, 2016. A Systematic Review on the Role of Artificial Intelligence in Sonographic Diagnosis of Thyroid Cancer 280 FBT, Vol. 7, No. 4 (2020) 266-280
45- Redmon J Darknet: Open Source Neural Networks in C, http://pjreddie.com/darknet/.
46- Song J, Chai YJ, Masuoka H, Park S-W, Kim S, Choi JY, Kong H-J, Lee KE, Lee J, Kwak N, others “Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules”. Medicine (Baltimore) 98:e15133, 2019.
47- Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z “ Rethinking the inception architecture for computer vision”, Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, June 27- 30, 2818- 2826, 2016.
48- Ko SY, Lee JH, Yoon JH, Na H, Hong E, Han K, Jung I, Kim E-K, Moon HJ, Park VY, others “Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound”. Head & Neck, 41:885–891, 2019.
49- Ma J, Wu F, Zhao Q, Kong D, others “Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks”. Int J Comput Assist Radiol Surg 12:1895–1910, 2017.
50- Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR “Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains”. J Clin Med 8:1976, 2019.
51- Song W, Li S, Liu J, Qin H, Zhang B, Shuyang Z, Hao A “Multi-task cascade convolution neural networks for automatic thyroid nodule detection and recognition”. IEEE J Biomed Heal informatics 23: 1215-1224, 2018.
52- Pedraza L, Vargas C, Narváez F, Durán O, Muñoz E, Romero E “An open access thyroid ultrasound image database”, 10th International Symposium on Medical Information Processing and Analysis, Cartagena de Indias, Colombia, October 14- 16, 92870W, 2015.
53- Lee JH, Baek JH, Kim JH, Shim WH, Chung SR, Choi YJ, Lee JH “Deep Learning--Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study”. Thyroid 28:1332–1338, 2018.
54- Wang J, Li S, Song W, Qin H, Zhang B, Hao A “Learning from Weakly-Labeled Clinical Data for Automatic Thyroid Nodule Classification in Ultrasound Images”, 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, October 7-10, 3114- 3118, 2018.
55- Zhu Y, Fu Z, Fei J “An image augmentation method using convolutional network for thyroid nodule classification by transfer learning”, 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, December 13- 16, 1819- 1823, 2017.
56- Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M “Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network”. J Digit Imaging, 30:477–486, 2017.
57- Liu T, Xie S, Yu J, Niu L, Sun W “Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features 2017” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.
58- Bibicu D, Moraru L, Biswas A “Thyroid nodule recognition based on feature selection and pixel classification methods.” J Digit Imaging, 26:119–128, 2013.
59- Abdolali F, Kapur J, Jaremko JL, Noga M, Hareendranathan AR, Punithakumar K “Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks”. Computers in Biology and Medicine 122:103871, 2020.
60- Halicek M, Dormer JD, Little JV, Chen AY, Fei B “Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning”. Biomedical Optics Express, 11(3):1383-400, 2020.
61- Yu X, Wang H, Ma L “Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning”. Current Medical Imaging, 16(2):174-80, 2020.
62- Kumar V, Webb J, Gregory A, Meixner DD, Knudsen JM, Callstrom M, Fatemi M, Alizad A “Automated Segmentation of Thyroid Nodule, Gland, and Cystic Components From Ultrasound Images Using Deep Learning”. IEEE Access 8:63482-96, 2020.
63- Cho J, Lee K, Shin E, Choy G, Do S “How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?” arXiv preprint arXiv:1511.06348, 2015.
64- Alwosheel A, van Cranenburgh S, Chorus CG “Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis”. Journal of choice modelling. 28:167-82, 2018.
2- Canadian Cancer Statistics Advisory Committee 2019 Canadian Cancer Statistics, http://www.cancer.ca/~/media/cancer.ca/CW/cancer%20 information/cancer%20101/Canadian%20cancer%20stati stics/Canadian-Cancer-Statistics-2018-EN.pdf?la=en.
3- Topstad D, Dickinson JA “Thyroid cancer incidence in Canada: a national cancer registry analysis”. C open 5:E612, 2017.
4- Danaei M, Haghdoost A, Momeni M. “An Epidemiological Review of Common Cancers in Iran; A Review Article”. Iranian Journal of Blood and Cancer 11(3):77-84, 2019.
5- Iran Cancer Statistics 2018 World Health Organization, https://gco.iarc.fr/today/data/factsheets/populations/364- iran-islamic-republic-of-fact-sheets.pdf 6- Anil G, Hegde A, Chong FV “Thyroid nodules: risk stratification for malignancy with ultrasound and guided biopsy”. Cancer Imaging, 11: 209, 2011.
7- Grant EG, Tessler FN, Hoang JK, Langer JE, Beland MD, Berland LL, Cronan JJ, Desser TS, Frates MC, Hamper UM, others “Thyroid ultrasound reporting lexicon: white paper of the ACR thyroid imaging, reporting and data system (TIRADS) committee”. J Am Coll Radiol, 12:1272–1279, 2015.
8- Li J, Ma X, Cui K Re:“ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI RADS Committee.” J Am Coll Radiol 15:380–381, 2018.
9- Acharya UR, Swapna G, Sree SV, Molinari F, Gupta S, Bardales RH, Witkowska A, Suri JS “A review on ultrasound-based thyroid cancer tissue characterization and automated classification”. Technol Cancer Res Treat 13:289–301, 2014.
10- Khachnaoui H, Guetari R, Khlifa N “A review on Deep Learning in thyroid ultrasound Computer-Assisted Diagnosis systems”. IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France, December 12-14, 291-297, 2018.
11- Sollini M, Cozzi L, Chiti A, Kirienko M “Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand?”. Eur J Radiol, 99:1–8, 2018.
12- Verburg F, Reiners C “Sonographic diagnosis of thyroid cancer with support of AI”. Nature Reviews Endocrinology, 15:1, 2019.
13- Zhang B, Tian J, Pei S, Chen Y, He X, Dong Y, Zhang L, Mo X, Huang W, Cong S, others “Machine Learning-- Assisted System for Thyroid Nodule Diagnosis”. Thyroid 29: 858-867, 2019.
14- Xia J, Chen H, Li Q, Zhou M, Chen L, Cai Z, Fang Y, Zhou H “Ultrasound-based differentiation of malignant and benign thyroid nodules: an extreme learning machine approach”. Comput Methods Programs Biomed, 147:37– 49, 2017.
15- Patricio M, Oliveira C, Caseiro-Alves F “Differentiating malignant thyroid nodule with statistical classifiers based on demographic and ultrasound features”, IEEE 5th Portuguese Meeting on Bioengineering (ENBENG), Coimbra, Portugal, February 16- 18, 1-4, 2017.
16- Illanes A, Esmaeili N, Poudel P, Balakrishnan S, Friebe M “Parametrical modelling for texture characterization— A novel approach applied to ultrasound thyroid segmentation”. PLoS One 14:e0211215, 2019.
17- China D, Illanes A, Poudel P, Friebe M, Mitra P, Sheet D “Anatomical structure segmentation in ultrasound volumes using cross frame belief propagating iterative random walks”. IEEE J Biomed Heal informatics 23: 1110- 1118, 2018.
18- Koundal D, Gupta S, Singh S “Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set”. Appl Soft Comput 40:86–97, 2016.
19- Narayan NS, Marziliano P, Kanagalingam J, Hobbs CGL “Speckle patch similarity for echogenicity-based multiorgan segmentation in ultrasound images of the thyroid gland”. IEEE J Biomed Heal informatics 21:172– 183, 2015.
20- Chang C-Y, Lei Y-F, Tseng C-H, Shih S-R “Thyroid segmentation and volume estimation in ultrasound images”. IEEE Trans Biomed Eng 57:1348–1357, 2010.
21- Prochazka A, Gulati S, Holinka S, Smutek D “Patch based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition”. Comput Med Imaging Graph, 71:9–18, 2019.
22- Yu Q, Jiang T, Zhou A, Zhang L, Zhang C, Xu P “Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images”. Eur Arch Oto-rhino-laryngology, 274:2891–2897, 2017. 23- “ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform”. Comput Methods Programs Biomed 107:233–241, 2012.
24- Tsantis S, Dimitropoulos N, Cavouras D, Nikiforidis G “Morphological and wavelet features towards sonographic thyroid nodules evaluation”. Comput Med Imaging Graph, 33:91–99, 2009.
25- Ding J, Cheng HD, Huang J, Zhang Y, Liu J “An improved quantitative measurement for thyroid cancer detection based on elastography”. Eur J Radiol, 81:800– 805, 2012.
26- Acharya UR, Chowriappa P, Fujita H, Bhat S, Dua S, Koh JEW, Eugene LWJ, Kongmebhol P, Ng K-H “Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images”. Knowledge-Based Syst, 107:235– 245, 2016.
27- Chang Y, Paul AK, Kim N, Baek JH, Choi YJ, Ha EJ, Lee KD, Lee HS, Shin D, Kim N “Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments”. Med Phys 43:554–567, 2016.
28- Chang C-Y, Chen S-J, Tsai M-F “Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images”. Pattern Recognit, 43:3494–3506, 2010.
29- Koundal D, Gupta S, Singh S “Computer aided thyroid nodule detection system using medical ultrasound images”. Biomed Signal Process Control, 40:117–130, 2018.
30- Ouyang F, Guo B, Ouyang L, Liu Z, Lin S, Meng W, Huang X, Chen H, Qiu-gen H, Yang S “Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules”. Eur J Radiol, 113:251–257, 2019.
31- Poudel P, Illanes A, Sheet D, Friebe M “Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches”. J Healthc Eng, 1-13, 2018.
32- Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O “3D U-Net: learning dense volumetric segmentation from sparse annotation”, International Conference on Medical Image Computing and Computer Assisted Intervention, Athens, Greece, October 17-21, 424-432, 2016.
33- Wunderling T, Golla B, Poudel P, Arens C, Friebe M, Hansen C “Comparison of thyroid segmentation techniques for 3D ultrasound Medical Imaging”. Image Processing: 10133: 1013317, 2017.
34- Zhou S, Wu H, Gong J, Le T, Wu H, Chen Q, Xu Z “Mark-Guided Segmentation of Ultrasonic Thyroid Nodules Using Deep Learning Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine”. Chengdu, China, October 13-15, 21- 26, 2018.
35- Ronneberger O, Fischer P, Brox T “U-net: Convolutional networks for biomedical image segmentation International Conference on Medical image computing and computer-assisted intervention”. Munich, Germany, October 5-9, 234-241, 2015.
36- Ma J, Wu F, Zhu J, Kong D, others “Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images”. Med Phys 44:1678–1691, 2017.
37- Li X, Wang S, Wei X, Zhu J, Yu R, Zhao M, Yu M, Liu Z, Liu S “Fully Convolutional Networks for Ultrasound Image Segmentation of Thyroid Nodules IEEE 20th International Conference on High Performance Computing and Communications”; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, June 28-30, 886- 890, 2018.
38- Ren S, He K, Girshick R, Sun J “Faster r-cnn: Towards real-time object detection with region proposal networks Advances in neural information processing systems”. Montreal, Quebec, Canada, December 7-12, 91-99, 2015.
39- Li H, Weng J, Shi Y, Gu W, Mao Y, Wang Y, Liu W, Zhang J “An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images”. Sci Rep 8: 1-12, 2018.
40- Redmon J, Farhadi A “YOLO9000: better, faster, stronger” Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, United States, July 21-26, 7263- 7271, 2017.
41- Szegedy C, Ioffe S, Vanhoucke V, Alemi AA “Inception-v4, inception-resnet and the impact of residual connections on learning”, Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, February 4–9, 4278- 4284, 2017.
42- Wang L, Yang S, Yang S, Zhao C, Tian G, Gao Y, Chen Y, Lu Y “Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network”. World J Surg Oncol 17:12, 2019.
43- Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, Xin X, Qin C, Wang X, Li J, others “Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study”. Lancet Oncol 20:193–201, 2019.
44- He K, Zhang X, Ren S, Sun J “Deep residual learning for image recognition”, Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, June 27-30, 770- 778, 2016. A Systematic Review on the Role of Artificial Intelligence in Sonographic Diagnosis of Thyroid Cancer 280 FBT, Vol. 7, No. 4 (2020) 266-280
45- Redmon J Darknet: Open Source Neural Networks in C, http://pjreddie.com/darknet/.
46- Song J, Chai YJ, Masuoka H, Park S-W, Kim S, Choi JY, Kong H-J, Lee KE, Lee J, Kwak N, others “Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules”. Medicine (Baltimore) 98:e15133, 2019.
47- Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z “ Rethinking the inception architecture for computer vision”, Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, June 27- 30, 2818- 2826, 2016.
48- Ko SY, Lee JH, Yoon JH, Na H, Hong E, Han K, Jung I, Kim E-K, Moon HJ, Park VY, others “Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound”. Head & Neck, 41:885–891, 2019.
49- Ma J, Wu F, Zhao Q, Kong D, others “Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks”. Int J Comput Assist Radiol Surg 12:1895–1910, 2017.
50- Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR “Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains”. J Clin Med 8:1976, 2019.
51- Song W, Li S, Liu J, Qin H, Zhang B, Shuyang Z, Hao A “Multi-task cascade convolution neural networks for automatic thyroid nodule detection and recognition”. IEEE J Biomed Heal informatics 23: 1215-1224, 2018.
52- Pedraza L, Vargas C, Narváez F, Durán O, Muñoz E, Romero E “An open access thyroid ultrasound image database”, 10th International Symposium on Medical Information Processing and Analysis, Cartagena de Indias, Colombia, October 14- 16, 92870W, 2015.
53- Lee JH, Baek JH, Kim JH, Shim WH, Chung SR, Choi YJ, Lee JH “Deep Learning--Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study”. Thyroid 28:1332–1338, 2018.
54- Wang J, Li S, Song W, Qin H, Zhang B, Hao A “Learning from Weakly-Labeled Clinical Data for Automatic Thyroid Nodule Classification in Ultrasound Images”, 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, October 7-10, 3114- 3118, 2018.
55- Zhu Y, Fu Z, Fei J “An image augmentation method using convolutional network for thyroid nodule classification by transfer learning”, 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, December 13- 16, 1819- 1823, 2017.
56- Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M “Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network”. J Digit Imaging, 30:477–486, 2017.
57- Liu T, Xie S, Yu J, Niu L, Sun W “Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features 2017” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.
58- Bibicu D, Moraru L, Biswas A “Thyroid nodule recognition based on feature selection and pixel classification methods.” J Digit Imaging, 26:119–128, 2013.
59- Abdolali F, Kapur J, Jaremko JL, Noga M, Hareendranathan AR, Punithakumar K “Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks”. Computers in Biology and Medicine 122:103871, 2020.
60- Halicek M, Dormer JD, Little JV, Chen AY, Fei B “Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning”. Biomedical Optics Express, 11(3):1383-400, 2020.
61- Yu X, Wang H, Ma L “Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning”. Current Medical Imaging, 16(2):174-80, 2020.
62- Kumar V, Webb J, Gregory A, Meixner DD, Knudsen JM, Callstrom M, Fatemi M, Alizad A “Automated Segmentation of Thyroid Nodule, Gland, and Cystic Components From Ultrasound Images Using Deep Learning”. IEEE Access 8:63482-96, 2020.
63- Cho J, Lee K, Shin E, Choy G, Do S “How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?” arXiv preprint arXiv:1511.06348, 2015.
64- Alwosheel A, van Cranenburgh S, Chorus CG “Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis”. Journal of choice modelling. 28:167-82, 2018.
| Files | ||
| Issue | Vol 7 No 4 (2020) | |
| Section | Literature (Narrative) Review(s) | |
| DOI | https://doi.org/10.18502/fbt.v7i4.5324 | |
| Keywords | ||
| Artificial Intelligence Computer Aided Diagnosis Classification Deep Learning Medical Ultrasound Analysis Segmentation Thyroid | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Abdolali F, Shahroudnejad A, Amiri S, Rakkunedeth Hareendranathan A, L Jaremko J, Noga M, Punithakumar K. A Systematic Review on the Role of Artificial Intelligence in Sonographic Diagnosis of Thyroid Cancer: Past, Present and Future. Frontiers Biomed Technol. 2020;7(4):266-280.
