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2019 | OriginalPaper | Chapter

Measuring the Angle of Hallux Valgus Using Segmentation of Bones on X-Ray Images

Authors : Konrad Kwolek, Henryk Liszka, Bogdan Kwolek, Artur Gądek

Published in: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions

Publisher: Springer International Publishing

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Abstract

Hallux valgus is a common feet problem. A hallux valgus deformity is when there is medial deviation of the first metatarsal and lateral deviation of the great toe. In this work, we introduce an algorithm for automatic recognition of hallux valgus on X-ray images with feet. The bones are segmented on the basis of U-Net convolutional neural network. The neural network has been trained on thirty manually segmented images by an orthopedist. We present both qualitative and quantitative segmentation results on ten test images. We present algorithms for great toe extraction and hallux valgus angle (HVA) estimation. The HVA is estimated as the angle between two lines fitted to big toe skeleton. We compare results that were obtained manually, by computer-assisted programs that are used by radiologists, and by the proposed algorithm.

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Literature
1.
Dechter, R.: Learning while searching in constraint-satisfaction-problems. In: Proceedings of the Fifth AAAI National Conference on Artificial Intelligence, pp. 178–183. AAAI Press (1986)
2.
Aizenberg, I., Aizenberg, N., Vandewalle, J.: Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Kluwer Academic Publishers, Norwell (2000)CrossRef
3.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef
4.
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)CrossRef
5.
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823 (2015)
6.
Fogel, A., Kvedar, J.: Artificial intelligence powers digital medicine. NPJ Digit. Med. 1(1), 5 (2018)CrossRef
7.
Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25(1), 44–56 (2019)CrossRef
8.
Mayo, R.C., Leung, J.: Artificial intelligence and deep learning - radiology’s Next frontier? Clin. Imaging 49, 87–88 (2018)CrossRef
9.
Fazal, M.I., Patel, M.E., Tye, J., Gupta, Y.: The past, present and future role of artificial intelligence in imaging. Eur. J. Radiol. 105, 246–250 (2018)CrossRef
10.
Liew, C.: The future of radiology augmented with artificial intelligence: a strategy for success. Eur. J. Radiol. 102, 152–156 (2018)CrossRef
11.
Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci. Rep. 8(1) (2018)
12.
Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-Rays with deep learning. CoRR abs/1711.05225 (2017)
13.
Islam, J., Zhang, Y.: Towards robust lung segmentation in chest radiographs with deep learning. CoRR abs/1811.12638 (2018)
14.
15.
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8, 1 (2018) CrossRef
16.
Wu, J., Mahfouz, M.R.: Robust X-ray image segmentation by spectral clustering and active shape model. J. Med. Imaging 3 (2016)CrossRef
17.
Stolojescu-Crisan, C., Stefan, H.: An interactive X-ray image segmentation technique for bone extraction. In: International Work-Conference on Bioinformatics and Biomedical Engineering, pp. 1164–1171 (2014)
18.
Mohammadi, H.M., de Guise, J.A.: Enhanced X-ray image segmentation method using prior shape. IET Comput. Vision 11(2), 145–152 (2017)CrossRef
19.
Liszka, H., Gądek, A.: Results of scarf osteotomy without implant fixation in the treatment of hallux valgus. Foot Ankle Int. 39(11), 1320–1327 (2018)CrossRef
20.
Dinato, M., de Faria Freitas, M., Milano, C., Valloto, E., Ninomiya, A.F., Pagnano, R.G.: Reliability of two smartphone applications for radiographic measurements of hallux valgus angles. J. Foot Ankle Surg. 56(2), 230–233 (2017)CrossRef
21.
Srivastava, S., Chockalingam, N., Fakhri, T.E.: Radiographic measurements of hallux angles: a review of current techniques. Foot 20(1), 27–31 (2010)CrossRef
22.
Heineman, N., Chhabra, A., Zhang, L., Dessouky, R., Wukich, D.: Point vs. traditional method evaluation of hallux valgus: interreader reliability and intermethod performance using X-ray and MRI. Skeletal Radiol. 48(2), 251–257 (2019)CrossRef
23.
Yang, W., et al.: Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med. Image Anal. 35, 421–433 (2017)CrossRef
24.
Olczak, J., et al.: Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 88(6), 581–586 (2017)CrossRef
25.
Wülker, N., Mittag, F.: The treatment of hallux valgus. Deutsches Ärzteblatt Int. 109(49), 857–868 (2012)
26.
Garrow, A.P., Papageorgiou, A., Silman, A.J., Thomas, E., Jayson, M.I.V., Macfarlane, G.J.: The grading of hallux valgus. The Manchester scale. J. Am. Podiatr. Med. Assoc. 91(2), 74–78 (2001)CrossRef
27.
Lee, K.M., Ahn, S., Chung, C.Y., Sung, K., Park, M.: Reliability and relationship of radiographic measurements in hallux valgus. Clin. Orthop. Relat. Res. 470(9), 2613–2621 (2012)CrossRef
28.
Schneider, W., Csepan, R., Knahr, K.: Reproducibility of the radiographic metatarsophalangeal angle in hallux surgery. J. Bone Joint Surg. Am. 85–A, 494–499 (2003)CrossRef
29.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)
30.
31.
Metadata
Title
Measuring the Angle of Hallux Valgus Using Segmentation of Bones on X-Ray Images
Authors
Konrad Kwolek
Henryk Liszka
Bogdan Kwolek
Artur Gądek
Copyright Year
2019
Book
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions

Print ISBN: 978-3-030-30492-8

Electronic ISBN: 978-3-030-30493-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-30493-5_32

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