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Capsule Networks Based Automatic Measurement System for Scoliosis

Yıl 2021, Cilt: 9 Sayı: 5, 2087 - 2101, 31.10.2021

Sena Goral Utku Köse

https://doi.org/10.29130/dubited.919890

Öz

Scoliosis is a disease that deforms the general spine structureas a result of curvature of the spine. Although there are various methods in the diagnosis and treatment of the Scoliosis, the main purpose is to reduce the curvature angle, which is calledas Cobbangle, within the framework of reducing the level of Scoliosis. Cobbangle measurement is essentially performed manually on the spine x-rays by the expert. However, automating this process with an Artificial Intelligence approach such as deeplearning will provide great convenience and precision for both patient and the expert. Based on the explanations, the current status of the literature in terms of Scoliosis and deeplearning-focused studies was firstly discussed in this study, and then the Cobbangle measurements were automated with a Capsule Network (CapsNet)-based solution. As a result of the comparison of the CapsNet solution with the ConvNet, BoostNet, RFR and ResNet-50 models, it was determined that the CapsNet model gave the best findings

Anahtar Kelimeler

Scoliosis Spinal curvature Cobb angle Capsule networks CapsNet Deep learning

Kaynakça

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  • [2] D. Sevimli, M. Sanrı and M. Altuğ, “The effect of corrective exercises on the treatment of a scoliosis patient: Acase report,” International Congress on Education (ERPA-2016), 2016, pp. 40–42.
  • [3] M. Aebi, “The adult scoliosis, ”European Spine Journal, vol. 14, no. 10, pp. 925-948, 2005.
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Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi

Yıl 2021, Cilt: 9 Sayı: 5, 2087 - 2101, 31.10.2021

Sena Goral Utku Köse

https://doi.org/10.29130/dubited.919890

Öz

Skolyoz, omurganın eğrilmesi ile birlikte omurga genel yapısını deforme eden bir hastalıktır. Skolyoz tanı ve tedavi aşamasında çeşitli yöntemler olmakla birlikte, temel amaç Cobb açısı adı verilen eğrilik açısını azaltarak Skolyoz seviyesini düşürme çerçevesinde şekillenmektedir. Cobb açısı ölçümü esasında uzman tarafından, omurga röntgen filmleri üzerinde manuel olarak gerçekleştirilmektedir. Ancak bu sürecin derin öğrenme gibi bir Yapay Zeka yaklaşımıyla otomatikleştirilmesi hem hasta hem de uzman açısından büyük kolaylık ve kesinlik sağlayacaktır. Açıklamalardan hareketle bu çalışmada, öncelikli olarak Skolyoz ve derin öğrenme odaklı çalışmalar açısından literatürün güncel durumu ele alınmış, ardından Kapsül Ağları (CapsNet) tabanlı bir çözüm ile Cobb açısı ölçümlerinin otomatik bir hale getirilmesi sağlanmıştır. CapsNet çözümünün, ConvNet, BoostNet, RFR ve ResNet-50 modelleri ile karşılaştırılması neticesinde en iyi bulguları CapsNet modelinin verdiği tespit edilmiştir.

Anahtar Kelimeler

Skolyoz Omurga eğriliği Cobb açısı Kapsül ağları CapsNet Derin öğrenme

Kaynakça

  • [1] J. Jin, “Screening for scoliosis in adolescents,” JAMA: The Journal of the American Medical Association, vol. 319, no. 2, pp. 202–202, 2018.
  • [2] D. Sevimli, M. Sanrı and M. Altuğ, “The effect of corrective exercises on the treatment of a scoliosis patient: Acase report,” International Congress on Education (ERPA-2016), 2016, pp. 40–42.
  • [3] M. Aebi, “The adult scoliosis, ”European Spine Journal, vol. 14, no. 10, pp. 925-948, 2005.
  • [4] M. T. Hresko, “Idiopathic scoliosis in adolescents,” New England Journal of Medicine, vol. 368, no. 9, pp. 834-841, 2013.
  • [5] Y. U. İbişoğlu ve F. A. Çaliş, “İzmir ili Bornova ilçesi ilköğretim kurumlarında okuyan 12-14 yaş grubu cocuklarda skolyoz prevalansı,” Türkiye Fiziksel Tıp ve Rehabilitasyon Dergisi, c. 58, s. 2, ss. 109–113, 2012.
  • [6] C. Varol, “Skolyozlu olgularda egzersizin solunum fonksiyonlarına ve yaşam kalitesine etkisi,” Yüksek lisans tezi, Kardiyopulmoner Fizyoterapi ve Rehabilitasyon, Marmara Üniversitesi, İstanbul, Türkiye, 2019.
  • [7] A. C. Kittleson and L. W. Lim, “Measurement of scoliosis,” American Journal of Roentgenology, vol. 108, no. 4, pp. 775–777, 1970.
  • [8] H. G. Yılmaz, “İdiyopatik skolyozda egzersiz reçeteleme,” Türkiye Fiziksel Tıp ve Rehabilitasyon Dergisi, c. 60, s. özel sayı 2, ss. S31–S35, 2014.
  • [9] M. Bakator and D. Radosav, “Deeplearning and medical diagnosis: A review of literature,” Multimodal Technologies and Interaction, vol. 2, no. 3, pp. 47, 2018.
  • [10] O. Deperlioglu, “Classification of segmented phonocardiograms by convolutional neural networks,” BRAIN: Broad Research in Artificial Intelligence and Neuroscience, vol. 10, no. 2, pp. 5-13, 2019.
  • [11] D. Ravi, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo and G. Z. Yang, “Deeplearning for healthin formatics,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4-21, 2016.
  • [12] G. E. Guraksin, S. Barin, E. Ozgul and F. Kaya, “COVID-19 diagnosis using deeplearning,” Düzce University Journal of Science & Technology, vol. 9, no. 3, pp. 8-23, 2021.
  • [13] J. Hemanth, U. Kose, O. Deperlioglu and V. H. C. de Albuquerque, “An augmented reality-supported mobile application for diagnosis of heart diseases,” The Journal of Supercomputing, vol. 76, no. 2, pp. 1242-1267, 2020.
  • [14] A. B. Levine, C. Schlosser, J. Grewal, R. Coope, S. J. Jones and S. Yip, “Rise of the machines: Advances in deeplearning for cancer diagnosis,” Trends in Cancer, vol. 5, no. 3, pp. 157-169, 2019.
  • [15] A. Helwan, G. El-Fakhri, H. Sasani and D. Uzun Ozsahin, “Deep networks in identifying CT brain hemorrhage,” Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2215-2228, 2018.
  • [16] J. Hemanth, O. Deperlioglu and U. Kose, “An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network,” Neural Computing and Applications, vol. 32, no. 3, pp. 707-721, 2020.
  • [17] U. M. Prakash, K. Kottursamy, K. Cengiz, U. Kose and B. T. Hung, “4x-Expert systems for early predication of osteoporosis using multi-model algorithms,” Measurement, vol. 180, no. 8, pp. 109543, 2021.
  • [18] J. Alzubi, B. Bharathikannan, S. Tanwar, R. Manikandan, A. Khanna and C. Thaventhiran, “Boosted neural network ensemble classification for lung cancer disease diagnosis,” Applied Soft Computing, vol. 80, no. 7, pp. 579-591, 2019.
  • [19] M. I. Razzak, S. Naz and A. Zaib, “Deeplearning form edical image processing: Overview, challenges and the future,” in Classification in BioApps, 1st ed., vol. 26, Cham, Germany: Springer, 2018, pp. 323–350.
  • [20] S. Zhou, H. G. Kevin and S. Dinggang, Deep Learning for Medical Image Analysis, 1st ed., vol. 81, London, UK: Elsevier Academic Press, 2017, pp. 11–24.
  • [21] A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi, Y. Liu, E. Topol, J. Dean andR. Socher, “Deeplearning-enabled medical computervision,” Digital Medicine, vol. 4, no. 1, pp. 1–9, 2021.
  • [22] I. Castiglioni, L. Rundo, M. Codari, G. DiLeo, C. Salvatore, M. Interlenghi, F. Gallivanone, A. Cozzi, N. C. D’amico and F. Sardanelli, “AI applications to medical images: From machine learning to deep learning,” Physica Medica, vol. 83, no. 1, 9–24, 2021.
  • [23] E. Xi, S. Bing and Y. Jin, “Capsule network performance on complex data,” 2017, arXiv:1712.03480.
  • [24] P. Afshar, A. Mohammadi and K. N. Plataniotis, “Brain tumor type classification via capsule networks,” in 2018 25th IEEE International Conference on Image Processing (ICIP), 2018, pp. 3129–3133.
  • [25] Wikipedia. (2006, November 23). Omurga / Vertebral column. (Author: Arcadian) [Online]. Available: https://tr.wikipedia.org/wiki/Omurga
  • [26] Wikipedia. (2017, November 5). Thoracic vertebrae / A thoracic spine X-ray of a 57-year-old male. (Author: Who is johngalt). [Online]. Available: https://en.wikipedia.org/wiki/Thoracic_vertebrae
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  • [28] A. A. Süzen, Z. Yıldız, K. Kayaalp, O. Ceylanve E. Arabacı, “Skolyoz hastalığının tespiti için taşınabilir cihaz tasarımı,” Avrupa Bilim ve Teknoloji Dergisi, c. 15, s. 1, ss. 483–490, 2019.
  • [29] P. Patias, E. Stylianidis, M. Pateraki, Y. Chrysanthou, C. Contozis and T. Zavitsanakis, “3D digital photogrammetric reconstructions for scoliosis screening,” in ISPRS Commission Symposium on Image Engineering and Vision Metrology, 2006, pp. 1214–1218.
  • [30] Molinaspine. (2020, December 26). Adolescent Scoliosis. Domingo Molina IV, M.D. Spine Surgeon [Online]. Available: https://molinaspine.com/spine-deformity
  • [31] M. A. Asher and D. Burton, “Adolescent idiopathic scoliosis: natural history and long term treatment effects,” Scoliosis, vol. 1, no. 2, pp. 1–10, 2006.
  • [32] Ö. Aydıngöz, “Adolesand omurga sorunları,” İstanbul Üniversitesi Cerrahpaşa Tıp Fakültesi Sürekli Tıp Eğitimi Etkinlikleri Adolesan Sağlığı Sempozyumu, İstanbul, Türkiye, 2005, ss. 125–133.
  • [33] S. Langensiepen, O. Semler, R. Sobottke, O. Fricke, J. Franklin, E. Schönau and P. Eysel, “Measuring procedures to determine the cobbangle in idiopathic scoliosis: a systematic review,” European Spine Journal, vol. 22, no. 11, pp. 2360–2371, 2013.
  • [34] R. T. Morrissy, G. S. Goldsmith, E. C. Hall, D. Kehl, and G. H. Cowie, “Measurement of the cobbangle on radiographs of patients who have scoliosis. Evaluation of intrinsicerror,” The Journal of Bone and Joint Surgery, vol. 72, no. 3, 320–327, 1990.
  • [35] T. A. Sardjono, M. H. Wilkinson, A. G. Veldhuizen, P. M. vanOoijen, K. E. Purnama and G. J. Verkerke, “Automatic cobbangle determination from radiographic images,” Spine, vol. 38, no. 20, pp. E1256–E1262, 2013.
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  • [37] H. Kim, H. S. Kim, E. S. Moon, C. S. Yoon, T. S. Chung, H. T. Song, J. S. Suh, Y. H. Lee and S. Kim, “Scoliosis imaging: What radiologists should know—erratum,” Radiographics, vol. 35, no. 4, pp. 1316–1335, 2015.
  • [38] Y. Pan, Q. Chen, T. Chen, H. Wang, X. Zhu, Z. Fang and Y. Lu, “Evaluation of a computer-aided method form easuring the cobbangle on chest x-rays,” European Spine Journal, vol. 28, no. 12, pp. 3035–3043, 2019.
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  • [40] R. Choi, K. Watanabe, H. Jinguji, N. Fujita, Y. Ogura, S. Demura, T. Kotani, K. Wada, M. Miyazaki, H. Shigematsu, and Y. Aoki, “CNN-based spine and cobbangle estimator using moire images,” IIEEJ Transactions on Image Electronicsand Visual Computing, vol. 5, no. 2, pp. 135–144, 2017.
  • [41] K. Zhang, N. Xu, G. Yang and J. Wu, “An automated cobbangle estimation method using convolutional neural network with area limitation,” inMICCAI 2019:Medical Image Computing and Computer Assisted Intervention, 1st ed., vol. 11769, Cham, Germany: Springer, 2019, pp. 775–783.
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  • [58] T. Ungi, H. Greer, K. R. Sunderland, V. Wu, Z. M. Baum, C. Schlenger, M. Oetgen, K. Cleary, S. R. Aylward and G. Fichtinger, “Automatic spine ultrasound segmentation for scoliosis visualization and measurement,” IEEE Transactions on Biomedical Engineering, vol. 67, vol. 11, pp. 3234-3241, 2020.
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  • [60] M. K. Patrick, A. F. Adekoya, A. A. Mighty and B. Y. Edward, “Capsule networks–a survey,” Journal of King Saud University-Computer and Information Sciences, 2019, doi: 10.1016/j.jksuci.2019.09.014.
  • [61] D. De. (2017, November 1). What is a Caps Netor Capsule Network? [Online]. Available: https://medium.com/hackernoon/what-is-a-capsnet-or-capsule-network-2bfbe48769cc [62] H. Wu, C. Bailey, P. Rasoulinejad and S. Li, “Automatic land mark estimation for adolescent idiopathic scoliosis assessment using boostnet,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017, pp. 127–135.
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Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Sena Goral 0000-0001-9277-7623

Utku Köse 0000-0002-9652-6415

Yayımlanma Tarihi 31 Ekim 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 5

Kaynak Göster

APA Goral, S., & Köse, U. (2021). Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 9(5), 2087-2101. https://doi.org/10.29130/dubited.919890
AMA Goral S, Köse U. Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi. DÜBİTED. Ekim 2021;9(5):2087-2101. doi:10.29130/dubited.919890
Chicago Goral, Sena, ve Utku Köse. “Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 9, sy. 5 (Ekim 2021): 2087-2101. https://doi.org/10.29130/dubited.919890.
EndNote Goral S, Köse U (01 Ekim 2021) Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 5 2087–2101.
IEEE S. Goral ve U. Köse, “Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi”, DÜBİTED, c. 9, sy. 5, ss. 2087–2101, 2021, doi: 10.29130/dubited.919890.
ISNAD Goral, Sena - Köse, Utku. “Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9/5 (Ekim 2021), 2087-2101. https://doi.org/10.29130/dubited.919890.
JAMA Goral S, Köse U. Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi. DÜBİTED. 2021;9:2087–2101.
MLA Goral, Sena ve Utku Köse. “Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, c. 9, sy. 5, 2021, ss. 2087-01, doi:10.29130/dubited.919890.
Vancouver Goral S, Köse U. Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi. DÜBİTED. 2021;9(5):2087-101.
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