人工智能在影像學的發展、現狀及展望_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

 宋彬 , 黃子星

四川大學華西醫院放射科（成都 610041）;

Corresponding?author：

宋彬, Email: songbin.cjr@vip.163.com

Keywords：

DOI：

10.7507/1007-9424.201804080

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Citation： 宋彬, 黃子星. 人工智能在影像學的發展、現狀及展望. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2018, 25(5): 523-527. doi: 10.7507/1007-9424.201804080 Copy

1.	涂仕奎, 楊杰, 連勇, 等. 關于智能醫療研究與發展的思考. 科學, 2017, (3): 102-103.
2.	Mayo RC, Leung J. Artificial intelligence and deep learning—Radiology’s next frontier. Clin Imaging, 2018, 49: 87-88.
3.	Castellino RA. Computer aided detection (CAD): an overview. Cancer Imaging, 2005, 5: 17-19.
4.	DoiK. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph, 2007, 31(4-5): 198-211.
5.	Way TW, Sahiner B, Chan HP, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys, 2009, 36(7): 3086-3098.
6.	Firmino M, Angelo G, Morais H, et al. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online, 2016, 15: 2.
7.	Cascio D, Magro R, Fauci F, et al. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput Biol Med, 2012, 42(11): 1098-1109.
8.	Lodwick GS. Computer-aided diagnosis in radiology. A research plan. Invest Radiol, 1966, 1(1): 72-80.
9.	Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys, 2008, 35(12): 5799-5820.
10.	Giger ML. Computerized analysis of images in the detection and diagnosis of breast cancer. Semin Ultrasound CT MR, 2004, 25(5): 411-418.
11.	Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng, 2013, 15: 327-357.
12.	Rao VM, Levin DC, Parker L, et al. How widely is computer-aided detection used in screening and diagnostic mammography? J Am Coll Radiol, 2010, 7(10): 802-805.
13.	Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12 860 patients in a community breast center. Radiology, 2001, 220(3): 781-786.
14.	Alonzo-Proulx O, Packard N, Boone JM, et al. Validation of a method for measuring the volumetric breast density from digital mammograms. Phys Med Biol, 2010, 55(11): 3027-3044.
15.	van Engeland S, Snoeren PR, Huisman H, et al. Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging, 2006, 25(3): 273-282.
16.	Huo Z, Giger ML, Olopade OI, et al. Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. Radiology, 2002, 225(2): 519-526.
17.	Manduca A, Carston MJ, Heine JJ, et al. Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol Biomarkers Prev, 2009, 18(3): 837-845.
18.	Nielsen M, Karemore G, Loog M, et al. A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer. Cancer Epidemiol, 2011, 35(4): 381-387.
19.	Li H, Giger ML, Olopade OI, et al. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol, 2007, 14(5): 513-521.
20.	Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol, 2017, 28(6): 1191-1206.
21.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
22.	Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med, 2017, 38: 122-139.
23.	Chen W, Giger ML, Bick U, et al. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys, 2006, 33(8): 2878-2887.
24.	Chen W, Giger ML, Li H, et al. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med, 2007, 58(3): 562-571.
25.	Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer, 2016, 2. pii: 16012.
26.	Napel S, Giger M. Special section guest editorial:radiomics and imaging genomics: quantitative imaging for precision medicine. J Med Imaging (Bellingham), 2015, 2(4): 041001.
27.	Gutman DA, Cooper LA, Hwang SN, et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology, 2013, 267(2): 560-569.
28.	Karlo CA, Di Paolo PL, Chaim J, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology, 2014, 270(2): 464-471.
29.	Yamamoto S, Maki DD, Korn RL, et al. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am J Roentgenol, 2012, 199(3): 654-663.
30.	Mazurowski MA, Zhang J, Grimm LJ, et al. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology, 2014, 273(2): 365-372.
31.	Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging, 2015, 42(4): 902-907.
32.	Ashraf AB, Gavenonis SC, Daye D, et al. A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk. IEEE Trans Med Imaging, 2013, 32(4): 637-648.
33.	Ashraf AB, Daye D, Gavenonis S, et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology, 2014, 272(2): 374-384.
34.	Bishop CM. Pattern recognition and machine learning. Springer, 2006: 137-218 .
35.	Ba?tanlar Y, Ozuysal M. Introduction to machine learning. Methods Mol Biol, 2014, 1107: 105-128.
36.	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
37.	Kansagra AP, Yu JP, Chatterjee AR, et al. Big data and the future of radiology informatics. Acad Radiol, 2016, 23(1): 30-42.
38.	Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255-260.
39.	Zhang W, DoiK, Giger ML, et al. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med Phys, 1994, 21(4): 517-524.
40.	Sahiner B, Chan HP, Petrick N, et al. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging, 1996, 15(5): 598-610.
41.	Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
42.	Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017, 19: 221-248.
43.	Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Communic ACM, 2017, 60(6): 84-90.
44.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Computer Science, 2014.
45.	Shao L, Zhu F, Li X. Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst, 2015, 26(5): 1019-1034.
46.	Yang Q, Pan SJ. A survey on transfer learning. IEEE Trans Knowle Data Engineering, 2009, 22(10): 1345-1359..
47.	Razavian AS, Azizpour H, Sullivan J, et al. CNN features off-the-shelf: an astounding baseline for recognition. Columbus: 2014 IEEE Conference on Computer Vision and Pattern Recongnition Workshops (CVPRW), 2014: 512-519.
48.	Donahue J, Jia Y, Vinyals O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition. Computer Science, 2013.
49.	Antropova N, Huynh BQ, Giger ML. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys, 2017, 44(10): 5162-5171.
50.	Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging (Bellingham), 2016, 3(3): 034501.

1. 涂仕奎, 楊杰, 連勇, 等. 關于智能醫療研究與發展的思考. 科學, 2017, (3): 102-103.
2. Mayo RC, Leung J. Artificial intelligence and deep learning—Radiology’s next frontier. Clin Imaging, 2018, 49: 87-88.
3. Castellino RA. Computer aided detection (CAD): an overview. Cancer Imaging, 2005, 5: 17-19.
4. DoiK. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph, 2007, 31(4-5): 198-211.
5. Way TW, Sahiner B, Chan HP, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys, 2009, 36(7): 3086-3098.
6. Firmino M, Angelo G, Morais H, et al. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online, 2016, 15: 2.
7. Cascio D, Magro R, Fauci F, et al. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput Biol Med, 2012, 42(11): 1098-1109.
8. Lodwick GS. Computer-aided diagnosis in radiology. A research plan. Invest Radiol, 1966, 1(1): 72-80.
9. Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys, 2008, 35(12): 5799-5820.
10. Giger ML. Computerized analysis of images in the detection and diagnosis of breast cancer. Semin Ultrasound CT MR, 2004, 25(5): 411-418.
11. Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng, 2013, 15: 327-357.
12. Rao VM, Levin DC, Parker L, et al. How widely is computer-aided detection used in screening and diagnostic mammography? J Am Coll Radiol, 2010, 7(10): 802-805.
13. Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12 860 patients in a community breast center. Radiology, 2001, 220(3): 781-786.
14. Alonzo-Proulx O, Packard N, Boone JM, et al. Validation of a method for measuring the volumetric breast density from digital mammograms. Phys Med Biol, 2010, 55(11): 3027-3044.
15. van Engeland S, Snoeren PR, Huisman H, et al. Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging, 2006, 25(3): 273-282.
16. Huo Z, Giger ML, Olopade OI, et al. Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. Radiology, 2002, 225(2): 519-526.
17. Manduca A, Carston MJ, Heine JJ, et al. Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol Biomarkers Prev, 2009, 18(3): 837-845.
18. Nielsen M, Karemore G, Loog M, et al. A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer. Cancer Epidemiol, 2011, 35(4): 381-387.
19. Li H, Giger ML, Olopade OI, et al. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol, 2007, 14(5): 513-521.
20. Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol, 2017, 28(6): 1191-1206.
21. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
22. Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med, 2017, 38: 122-139.
23. Chen W, Giger ML, Bick U, et al. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys, 2006, 33(8): 2878-2887.
24. Chen W, Giger ML, Li H, et al. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med, 2007, 58(3): 562-571.
25. Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer, 2016, 2. pii: 16012.
26. Napel S, Giger M. Special section guest editorial:radiomics and imaging genomics: quantitative imaging for precision medicine. J Med Imaging (Bellingham), 2015, 2(4): 041001.
27. Gutman DA, Cooper LA, Hwang SN, et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology, 2013, 267(2): 560-569.
28. Karlo CA, Di Paolo PL, Chaim J, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology, 2014, 270(2): 464-471.
29. Yamamoto S, Maki DD, Korn RL, et al. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am J Roentgenol, 2012, 199(3): 654-663.
30. Mazurowski MA, Zhang J, Grimm LJ, et al. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology, 2014, 273(2): 365-372.
31. Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging, 2015, 42(4): 902-907.
32. Ashraf AB, Gavenonis SC, Daye D, et al. A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk. IEEE Trans Med Imaging, 2013, 32(4): 637-648.
33. Ashraf AB, Daye D, Gavenonis S, et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology, 2014, 272(2): 374-384.
34. Bishop CM. Pattern recognition and machine learning. Springer, 2006: 137-218 .
35. Ba?tanlar Y, Ozuysal M. Introduction to machine learning. Methods Mol Biol, 2014, 1107: 105-128.
36. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
37. Kansagra AP, Yu JP, Chatterjee AR, et al. Big data and the future of radiology informatics. Acad Radiol, 2016, 23(1): 30-42.
38. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255-260.
39. Zhang W, DoiK, Giger ML, et al. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med Phys, 1994, 21(4): 517-524.
40. Sahiner B, Chan HP, Petrick N, et al. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging, 1996, 15(5): 598-610.
41. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
42. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017, 19: 221-248.
43. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Communic ACM, 2017, 60(6): 84-90.
44. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Computer Science, 2014.
45. Shao L, Zhu F, Li X. Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst, 2015, 26(5): 1019-1034.
46. Yang Q, Pan SJ. A survey on transfer learning. IEEE Trans Knowle Data Engineering, 2009, 22(10): 1345-1359..
47. Razavian AS, Azizpour H, Sullivan J, et al. CNN features off-the-shelf: an astounding baseline for recognition. Columbus: 2014 IEEE Conference on Computer Vision and Pattern Recongnition Workshops (CVPRW), 2014: 512-519.
48. Donahue J, Jia Y, Vinyals O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition. Computer Science, 2013.
49. Antropova N, Huynh BQ, Giger ML. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys, 2017, 44(10): 5162-5171.
50. Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging (Bellingham), 2016, 3(3): 034501.

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CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

人工智能在影像學的發展、現狀及展望

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