Abstract
In this paper, we propose a feature extraction and machine learning method for the classification of ultrasound images obtained from lumbar spine of pregnant patients in the transverse plane. A set of features, including matching values and positions, appearance of black pixels within predefined windows along the midline, are extracted from the ultrasound images using template matching and midline detection. Artificial neural network is utilized to classify the bone images and interspinous images. The neural network is trained with 1000 images from 25 pregnant subjects and tested on 720 images from a separate set of 18 pregnant patients. A high success rate (96.95% on training set, 95.75% on validation set and 94.12% on test set) is achieved with the proposed method. The trained neural network further tested on 43 videos collected from 43 pregnant subjects and successfully identified the proper needle insertion site (interspinous region) in all of the cases. Therefore, the proposed method is able to identify the ultrasound images of lumbar spine in an automatic manner, so as to facilitate the anesthetists’ work to identify the needle insertion point precisely and effectively.
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References
Rawal, N.: Reg. Anesth. Pain Med. 37(3), 310–317 (2012)
Osterman, M.J.K., Martin, J.: Epidural and Spinal Anesthesia Use During Labor: 27-State Reporting Area, Centers for Disease Control and Preventnion (2008)
Le Coq, G., Ducot, B., Benhamou, D.: Risk factors of inadequate pain relief during epidural analgesia for labour and delivery. Can. J. Anaesth. 45(8), 719–723 (1998)
Paech, M.J., Godkin, R., Webster, S.: Complications of obstetric epidural analgesia and anaesthesia: a prospective analysis of 10,995 cases. Int. J. Obstet. Anesth. 7(1), 5–11 (1998)
La Grange, P., Foster, P.A., Pretorius, L.K.: Application of the Doppler ultrasound bloodflow detector in supraclavicular brachial plexus block. Br. J. Anaesth. 50(9), 965–967 (1978)
Grau, T., Leipold, R.W., Conradi, R., Martin, E., Motsch, J.: Efficacy of ultrasound imaging in obstetric epidural anesthesia. J. Clin. Anesth. 14(3), 169–175 (2002)
Ecimovic, P., Loughrey, J.: Ultrasound in obstetric anaesthesia: a review of current applications. Int. J. Obstet. Anesth. 19(3), 320–326 (2010)
Noble, J.A., Navab, N., Becher, H.: Ultrasonic image analysis and image-guided interventions. Interface Focus 1(4), 673–685 (2011)
Tran, D., Rohling, R.: Automatic detection of lumbar anatomy in ultrasound images of human subjects. IEEE Trans. Biomed. Eng. 57(9), 2248–2256 (2010)
Kerby, B., Rohling, R., Nair, V., Abolmaesumi, P.: Automatic identification of lumbar level with ultrasound. In: Conf Proc. IEEE Eng. Med. Biol. Soc., pp. 2980–2983 (2008)
Al-Deen Ashab, H., Lessoway, V.A., Khallaghi, S., Cheng, A., Rohling, R., Abolmaesumi, P.: An augmented reality system for epidural anesthesia (AREA): prepuncture identification of vertebrae. IEEE Trans. Biomed. Eng. 60(9), 2636–2644 (2013)
Yu, S., Tan, K.K., Shen, C.Y., Sia, A.: Ultrasound Guided Automatic localization of needle insertion site for epidural anesthesia. In: Proceeding of IEEE International Conference on Mechatronics and Automation, pp. 985–990 (2013)
Lee, Y., Tanaka, M., Carvalho, J.: Sonoanatomy of the lumbar spine in patients with previous unintentional dural punctures during labour epidurals. Reg. Anesth. Pain. Med. 33(3), 266–270 (2008)
Carvalho, J.C.: Ultrasound-facilitated epidurals and spinals in obstetrics. Anesthesiol Clin. 26(1), 145–158 (2008)
Yu, S., Tan, K.K., Sng, B.L., Li, S.J., Sia, A.: Automatic identification of needle insertion site in epidural anesthesia with a cascading classifier. Ultrasound Med. Biol. (in press)
Maiorov, V., Pinkus, A.: Lower bounds for approximation by MLP neural networks. Neurocomputing 25(1), 81–91 (1999)
Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, IJCNN, pp. 593–605. IEEE (1989)
Haykin, S.: Neural networks and learning machines (vol. 3). Pearson Education, Upper Saddle River (2009)
Ding, S., Li, H., Su, C., Yu, J., Jin, F.: Evolutionary artificial neural networks: a review. Artif. Intell. Rev. 39, 251–260 (2013)
Alba, E., Chicano, J.F.: Training Neural Networks with GA Hybrid Algorithms. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 852–863. Springer, Heidelberg (2004)
David, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)
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Yu, S., Tan, K.K. (2014). Classification of Lumbar Ultrasound Images with Machine Learning. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_25
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DOI: https://doi.org/10.1007/978-3-319-13563-2_25
Publisher Name: Springer, Cham
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