||Diagnostics of Carpal Tunnel Syndrome Based on Features Extracted from Ultrasound Images by Deep Learning Technique
||Institute of Computer Science and Information Engineering
Carpal tunnel syndrome
腕隧道症候群是常見的手腕疾病，由於神經被壓迫所造成，在過去的研究中已經發現多個特徵與該疾病的相關性，如正中神經面積會因神經腫脹造成面積變大的情況、位移降低因結締組織纖維化。本研究中使用商用超音波儀器 Terason T3000 及 12L5的探頭頻率為12MHz對18位健康的受試者和21位病患進行腕部超音波影像掃描，
並自動分割超音波影像，結果為準確度70%，從影像中抽取參數，最後將參數作為輸入，使用人工智慧分類器((K-nearest neighbor、naïve Bayes、support vector machine、decision tree)進行分類，對比過去使用手動圈選的研究得到相近的準確度，而加入長寬比、堅固性參數後得到準確度的提升。分類器的結果顯示準確度在76%到85% 之間，其中KNN有最大的敏感度和特異度。本研究所提出的分類腕隧道症候群可用於輔助臨床診斷。
Carpal tunnel syndrome (CTS) is a kind of wrist neuropathy, which caused by elevated pressure in the carpal tunnel. In past studies, several features have been found to be related to the disease. For example, the median nerve area will be enlarged due to nerve swelling and decrease max displacement by fibrosis of connective tissue.
In this study, the commercial ultrasound instruments Terason T3000 and 12L5 were used with a probe frequency of 12MHz. 18 healthy subjects and 21 patients were scanned for wrist ultrasound images, and the ultrasound images were automatically segmented to extract parameters from the images. The result of segmentation is 70%. Finally, the parameters are used as input, and the artificial intelligence classifier (K-nearest neighbor, naïve Bayes, support vector machine, decision tree) is used for classification. Compared with the previous studies using manual labeling, the accuracy is similar, and the accuracy is improved after adding the aspect ratio and solidity parameters. The results show that the accuracy of the four classifiers is between 76% and 85%. The K-nearest neighbor has a maximum sensitivity of 90% and has a maximum specificity of 79%. The classification of carpal tunnel syndrome proposed in this study can be used to assist clinical diagnosis.
LIST OF FIGURES V
LIST OF TABLES VII
Charpter 1 INTRODUCTION 1
1.1 Foreword 1
1.2 Research background 1
1.3 Related works 2
1.3.1 Clinical diagnosis of CTS 2
1.3.2 Ultrasound imaging analysis for CTS 4
1.3.3 Segmentation 5
1.4 Motivation and objectives 5
Charpter 2 BACKGROUND 6
2.1 Fundamental of ultrasound 6
2.1.1 Fundamental of acoustic wave 6
2.1.2 Reflection and refraction 6
2.1.3 Attenuation 7
2.1.4 Ultrasonic transducers 8
2.2 Segmentation technique 11
2.3 Classification technique 11
2.3.1 Rule-based classifier 12
2.3.2 Computational intelligence classifier 12
2.4 Anatomical structure of carpal tunnel 12
Charpter 3 MATERIALS AND METHODS 14
3.1 Segmentation 14
3.2 Classifier 15
3.2.1 K-nearest neighbor classifier 15
3.2.2 Naïve Bayes classifier 17
3.2.3 Support vector machine 18
3.2.4 Decision tree 19
3.3 Wrist measurement with ultrasound 20
Charpter 4 RESULTS AND DISCUSSION 23
4.1 Segmentation 23
4.2 Data distribution 26
4.3 Ability of classification 31
Charpter 5 Conclusions 39
5.1 Conclusions 39
5.2 Future works 39
 K. K. Shung, Diagnostic ultrasound: Imaging and blood flow measurements. CRC press, 2015.
 J. N. Katz and B. P. Simmons, "Carpal tunnel syndrome," New England Journal of Medicine, vol. 346, no. 23, pp. 1807-1812, 2002.
 Y.-H. Lin, M.-Y. Hsieh, F.-C. Su, and S.-H. Wang, "Assessment of the kinetic trajectory of the median nerve in the wrist by high-frequency ultrasound," Sensors, vol. 14, no. 5, pp. 7738-7752, 2014.
 I. Ibrahim, W. Khan, N. Goddard, and P. Smitham, "Suppl 1: carpal tunnel syndrome: a review of the recent literature," The open orthopaedics journal, vol. 6, p. 69, 2012.
 I. Atroshi, C. Gummesson, R. Johnsson, E. Ornstein, J. Ranstam, and I. Rosen, "Prevalence of carpal tunnel syndrome in a general population," Jama, vol. 282, no. 2, pp. 153-158, 1999.
 L. Heller, H. Ring, H. Costeff, and P. Solzi, "Evaluation of Tinel’s and Phalen’s signs in diagnosis of the carpal tunnel syndrome," European neurology, vol. 25, no. 1, pp. 40-42, 1986.
 P. Seror, "Tinel’s sign in the diagnosis of carpal tunnel syndrome," Journal of Hand Surgery, vol. 12, no. 3, pp. 364-365, 1987.
 S. H. Kuschner, E. Ebramzadeh, D. Johnson, W. W. Brien, and R. Sherman, "Tinel's sign and Phalen's test in carpal tunnel syndrome," Orthopedics, vol. 15, no. 11, pp. 1297-1302, 1992.
 J. A. Durkan, "A new diagnostic test for carpal tunnel syndrome," J Bone Joint Surg Am, vol. 73, no. 4, pp. 535-8, 1991.
 J. Bruske, M. Bednarski, H. Grzelec, and A. Zyluk, "The usefulness of the Phalen test and the Hoffmann-Tinel sign in the diagnosis of carpal tunnel syndrome," Acta orthopaedica belgica, vol. 68, no. 2, pp. 141-145, 2002.
 D. Lee, M. T. van Holsbeeck, P. K. Janevski, D. L. Ganos, D. M. Ditmars, and V. B. Darian, "Diagnosis of carpal tunnel syndrome: ultrasound versus electromyography," Radiologic Clinics of North America, vol. 37, no. 4, pp. 859-872, 1999.
 J. C. White, S. R. Hansen, and R. K. Johnson, "A comparison of EMG procedures in the carpal tunnel syndrome with clinical‐EMG correlations," Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, vol. 11, no. 11, pp. 1177-1182, 1988.
 A. Q. A. Committee, C. K. Jablecki, C. M. T. Andary, Y. T. So, D. E. Wilkins, and F. H. Williams, "Literature review of the usefulness of nerve conduction studies and electromyography for the evaluation of patients with carpal tunnel syndrome," Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, vol. 16, no. 12, pp. 1392-1414, 1993.
 H. Gelmers, "The significance of Tinel's sign in the diagnosis of carpal tunnel syndrome," Acta Neurochirurgica, vol. 49, no. 3-4, pp. 255-258, 1979.
 S. TINEL’S, "Tinel’s sign and Phalen’s maneuver: physical signs of carpal tunnel syndrome," Hospital Physician, p. 39, 2000.
 E. Silvestri, C. Martinoli, L. E. Derchi, M. Bertolotto, M. Chiaramondia, and I. Rosenberg, "Echotexture of peripheral nerves: correlation between US and histologic findings and criteria to differentiate tendons," Radiology, vol. 197, no. 1, pp. 291-296, 1995.
 L. Padua et al., "Carpal tunnel syndrome: ultrasound, neurophysiology, clinical and patient-oriented assessment," Clinical Neurophysiology, vol. 119, no. 9, pp. 2064-2069, 2008.
 L. H. Visser, M. H. Smidt, and M. L. Lee, "High-resolution sonography versus EMG in the diagnosis of carpal tunnel syndrome," Journal of Neurology, Neurosurgery & Psychiatry, vol. 79, no. 1, pp. 63-67, 2008.
 N. Lam and A. Thurston, "Association of obesity, gender, age and occupation with carpal tunnel syndrome," Australian and New Zealand journal of surgery, vol. 68, no. 3, pp. 190-193, 1998.
 J. A. Kouyoumdjian, D. M. Zanetta, and M. P. Morita, "Evaluation of age, body mass index, and wrist index as risk factors for carpal tunnel syndrome severity," Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, vol. 25, no. 1, pp. 93-97, 2002.
 A. A. Ardakani et al., "Diagnosis of Carpal Tunnel Syndrome: A Comparative Study of Shear Wave Elastography, Morphometry and Artificial Intelligence Techniques," Pattern Recognition Letters, 2020.
 O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in International Conference on Medical image computing and computer-assisted intervention, 2015: Springer, pp. 234-241.
 T. N. Sainath, O. Vinyals, A. Senior, and H. Sak, "Convolutional, long short-term memory, fully connected deep neural networks," in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015: IEEE, pp. 4580-4584.
 A. Arbelle and T. R. Raviv, "Microscopy cell segmentation via convolutional LSTM networks," in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019: IEEE, pp. 1008-1012.
 J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431-3440.
 I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C. D. Spyropoulos, and P. Stamatopoulos, "Learning to filter spam e-mail: A comparison of a naive bayesian and a memory-based approach," arXiv preprint cs/0009009, 2000.
 P. C. Austin, J. V. Tu, J. E. Ho, D. Levy, and D. S. Lee, "Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes," Journal of clinical epidemiology, vol. 66, no. 4, pp. 398-407, 2013.
 V. A. Kumari and R. Chitra, "Classification of diabetes disease using support vector machine," International Journal of Engineering Research and Applications, vol. 3, no. 2, pp. 1797-1801, 2013.
 F. Wahid, R. K. Begg, C. J. Hass, S. Halgamuge, and D. C. Ackland, "Classification of Parkinson's disease gait using spatial-temporal gait features," IEEE journal of biomedical and health informatics, vol. 19, no. 6, pp. 1794-1802, 2015.
 S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, "Supervised machine learning: A review of classification techniques," Emerging artificial intelligence applications in computer engineering, vol. 160, no. 1, pp. 3-24, 2007.
 P. J. García-Laencina, J.-L. Sancho-Gómez, and A. R. Figueiras-Vidal, "Pattern classification with missing data: a review," Neural Computing and Applications, vol. 19, no. 2, pp. 263-282, 2010.
 J. M. Keller, M. R. Gray, and J. A. Givens, "A fuzzy k-nearest neighbor algorithm," IEEE transactions on systems, man, and cybernetics, no. 4, pp. 580-585, 1985.
 L. E. Peterson, "K-nearest neighbor," Scholarpedia, vol. 4, no. 2, p. 1883, 2009.
 Q. Wang, G. M. Garrity, J. M. Tiedje, and J. R. Cole, "Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy," Applied and environmental microbiology, vol. 73, no. 16, pp. 5261-5267, 2007.
 I. Rish, "An empirical study of the naive Bayes classifier," in IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001, vol. 3, no. 22, pp. 41-46.
 J. A. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural processing letters, vol. 9, no. 3, pp. 293-300, 1999.
 G. Cauwenberghs and T. Poggio, "Incremental and decremental support vector machine learning," in Advances in neural information processing systems, 2001, pp. 409-415.
 S. R. Safavian and D. Landgrebe, "A survey of decision tree classifier methodology," IEEE transactions on systems, man, and cybernetics, vol. 21, no. 3, pp. 660-674, 1991.
 M. A. Friedl and C. E. Brodley, "Decision tree classification of land cover from remotely sensed data," Remote sensing of environment, vol. 61, no. 3, pp. 399-409, 1997.
 A. Liaw and M. Wiener, "Classification and regression by randomForest," R news, vol. 2, no. 3, pp. 18-22, 2002.
 R. Díaz-Uriarte and S. A. De Andres, "Gene selection and classification of microarray data using random forest," BMC bioinformatics, vol. 7, no. 1, p. 3, 2006.
 C. Ledig et al., "Photo-realistic single image super-resolution using a generative adversarial network," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4681-4690.
 T. Kohonen, "The self-organizing map," Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990.
 H. S. Vasiliadis et al., "Microsurgical dissection of the carpal tunnel with respect to neurovascular structures at risk during endoscopic carpal tunnel release," Arthroscopy: The Journal of Arthroscopic & Related Surgery, vol. 22, no. 8, pp. 807-812, 2006.
 S. Standring et al., "Gray's anatomy: the anatomical basis of clinical practice," American journal of neuroradiology, vol. 26, no. 10, p. 2703, 2005.
 T. K. Cobb, B. K. Dalley, R. H. Posteraro, and R. C. Lewis, "Anatomy of the flexor retinaculum," The Journal of hand surgery, vol. 18, no. 1, pp. 91-99, 1993.
 A. Presazzi, C. Bortolotto, M. Zacchino, L. Madonia, and F. Draghi, "Carpal tunnel: Normal anatomy, anatomical variants and ultrasound technique," Journal of ultrasound, vol. 14, no. 1, pp. 40-46, 2011.
 H. Robbins, "Anatomical study of the median nerve in the carpal tunnel and etiologies of the carpal-tunnel syndrome," JBJS, vol. 45, no. 5, pp. 953-966, 1963.
 M. Mesgarzadeh, C. Schneck, and A. Bonakdarpour, "Carpal tunnel: MR imaging. Part I. Normal anatomy," Radiology, vol. 171, no. 3, pp. 743-748, 1989.
 D. Kumar, "Performance analysis of various data mining algorithms: A review," International Journal of Computer Applications, vol. 32, no. 6, pp. 9-16.
 J.-H. Lin, "Assessment of Kinetic Trajectory of the Median Nerve from Wrist Ultrasound Images in Patients with Carpal Tunnel Syndrome by Motion Tracking Technique," 2017.