Abstract
Accurate detection and diagnosis of developmental dysplasia of the hip (DDH), a common hip instability condition among infants, relies heavily on acquiring adequate ultrasound (US) image data. Although 2D US is the standard modality used for DDH screening, 3D US has recently been considered as well. Presently there is no automatic method (or even a standardized manual method) capable of analyzing the US volume to determine whether that volume is adequate for extracting DDH metrics required for diagnosis. Scan adequacy in 2D has seen only one work on automation and there has been no work done on scan adequacy in 3D. We propose an automatic, near real-time method of assessing 3D ultrasound scans in developmental dysplasia screening and diagnostic applications using a convolutional neural network (CNN). Our classifier labels volumes as adequate or inadequate for subsequent interpretation based on the presence of hip anatomy needed for DDH diagnosis. We validate our approach on 40 datasets from 15 pediatric patients and demonstrate a classification rate of 100% with average processing time of just above 2 s per US volume. We expect automatic US scan adequacy assessment to have significant clinical impact with the potential to help in imaging standardization, improving efficiency of measuring DDH metrics, and improving accuracy of clinical decision making.
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References
Atweh, L., Kan, J.: Multimodality imaging of developmental dysplasia of the hip. Pediatr. Radiol. 43(1), 166–171 (2013)
Graf, R., Mohajer, M., Florian, P.: Hip sonography update: quality-management, catastrophes - tips and tricks. Med. Ultrason. 15(4), 299–303 (2013)
Tschauner, C., Matthissen, H.: Hip sonography with graf-method in newborns: checklists help to avoid mistakes. OUB J. 1, 7–8 (2012)
Maraci, M., Bridge, C., Napolitano, R., Papageorghiou, A., Noble, A.: A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat. Med. Image Anal. 37, 22–36 (2017)
Rahmatullah, B., Papageorghiou, A., Noble, J.A.: Automated selection of standardized planes from ultrasound volume. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 35–42. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24319-6_5
Baumgartner, C.F., Kamnitsas, K., Matthew, J., Smith, S., Kainz, B., Rueckert, D.: Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 203–211. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_24
Quader, N., Hodgson, A., Mulpuri, K., Savage, T., Abugharbieh, A.: Automatic assessment of developmental dysplasia of the hip. In: IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 13–16 (2015)
Quader, N., Schaeffer, E., Mulpuri, K., Cooper, A., Hodgson, A., Abugharbieh, R.: Automatic evaluation of scan adequacy and dysplasia metrics in 2D ultrasound images of the neonatal hip. Bone Joint J. 98–B(sup. 21), 42 (2016)
Quader, N., Hodgson, A., Mulpuri, K., Schaeffer, E., Abugharbieh, R.: Automatic evaluation of scan adequacy and dysplasia metrics in 2-D ultrasound images of the neonatal hip. Ultras. Med. Biol. 43(6), 1252–1262 (2017)
Jaremko, J., Mabee, M., Swami, V., Jamieson, L., Chow, K., Thompson, R.: Potential for change in US diagnosis of hip dysplasia solely caused by changes in probe orientation: patterns of alpha-angle variation revealed by using three-dimensional US. Radiology 273(3), 870–878 (2014)
Mabee, M., Hareendranathan, A., Thompson, R., Dulai, S., Jaremko, J.: An index for diagnosing infant hip dysplasia using 3-D ultrasound: the acetabular contact angle. Pediatr. Radiol. 46(7), 1023–1031 (2016)
Hareendranathan, A., Mabee, M., Punithakumar, K., Noga, M., Jaremko, J.: A technique for semiautomatic segmentation of echogenic structures in 3D ultrasound, applied to infant hip dysplasia. Int. J. Comput. Assist. Radiol. Surg. 11(1), 31–42 (2015)
Quader, N., Hodgson, A., Mulpuri, K., Cooper, A., Abugharbieh, R.: Towards reliable automatic characterization of neonatal hip dysplasia from 3D ultrasound images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 602–609. Springer, Cham (2016). doi:10.1007/978-3-319-46720-7_70
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)
Krizhevski, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Neural Information Processing Systems, pp. 1097–1105 (2012)
Su, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)
Kingma, D., Ba, J.: Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations. arXiv preprint arXiv:1412.6980 (2015)
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Paserin, O., Mulpuri, K., Cooper, A., Hodgson, A.J., Abugharbieh, R. (2017). Automatic Near Real-Time Evaluation of 3D Ultrasound Scan Adequacy for Developmental Dysplasia of the Hip. In: Cardoso, M., et al. Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE CLIP 2017 2017. Lecture Notes in Computer Science(), vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_12
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DOI: https://doi.org/10.1007/978-3-319-67543-5_12
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