Machine learning-based approach for segmentation of intervertebral disc degeneration from lumbar section of spine using MRI images

• Autorzy: Shinde Jayashri V. , Joshi Yashwant V. , Manthalkar Ramchandra R. , Joshi

Identyfikatory

DOI

10.1515/bams-2020-0047

• Konferencja: 4th Jagiellonian Symposium on Advances in Particle Physics and Medicine, Krakow, 10-15 July 2022
• Języki publikacji: EN

Abstrakty

EN

Objectives: Intervertebral disc segmentation is one of the methods to diagnose spinal disease through the degener ation in asymptomatic and symptomatic patients. Even though numerous intervertebral disc segmentation tech niques are available, classifying the grades in the inter vertebral disc is a hectic challenge in the existing disc segmentation methods. Thus, an effective Whale Spine Generative Adversarial Network (WSpine-GAN) method is proposed to segment the intervertebral disc for effective grade classification. Methods: The proposed WSpine-GAN method effectively performs the disc segmentation, wherein the weights of Spine-GAN are optimally tuned using Whale Optimization Algorithm (WOA). Then, the refined disc features, such as pixel-based features and the connectivity features are extracted. Finally, the K-Nearest Neighbor (KNN) classifier based on the pfirrmann’s grading system performs the grade classification. Results: The implementation of the grade classification strategy based on the proposed WSpine-GAN and KNN is performed using the real-time database, and the perfor mance based on the metrics yielded the accuracy, true positive rate (TPR), and false positive rate (FPR) values of 97.778, 97.83, and 0.586% for the training percentage and 92.382, 90.580, and 1.972% for the K-fold value. Conclusions: The proposed WSpine-GAN method effec tively performs the disc segmentation by integrating the Spine-GANmethod and WOA. Here, the spinal cord images are segmented using the proposed WSpine-GAN method by tuning the weights optimally to enhance the performance of the disc segmentation.

Słowa kluczowe

EN

grade classification; intervertebral disc; KNN; segmentation; spine-GAN

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2022
• Tom: Vol. 18, no. 1
• Strony: 55--68
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Shinde Jayashri V.

• Department of Computer Engineering, Late G. N. Sapkal College of Engineering, Nashik, Maharashtra 422213, India

autor

Joshi Yashwant V.

• Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra, India

autor

Manthalkar Ramchandra R.

• Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra, India

autor

Joshi

• M.G.M.’s College of Engineering Kamothe, Navi Mumbai, Maharashtra, India

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Uwagi

Opublikowane przez Sciendo. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).