Abstract
The diagnosis of specific types of muscular dystrophies (MD) is mainly done through genetic testing. As this does not always provide an unambiguous result, muscle MRI images are often examined to confirm or verify the diagnosis as each MD type affects the muscles in a specific pattern. Different deep learning approaches (ResNet50 model pretrained on RadImageNet, auto-encoder model trained from scratch, segmentation U-Net model trained for muscle segmentation) were investigated to obtain image features from Dixon MRI of the proximal leg that were used for discriminating between cases with Becker Muscular Dystrophy (n = 18), Limb-Girdle Muscular Dystrophy R12 (n = 13) or no MD (n = 16). The results are compared with classification by a conventional random forest (RF) classifier using the fat fraction percentage per muscle as features. The RF classifier and the segmentation U-Net deep learning approach performed best with an average AUC of 0.957 and 0.934 respectively. Local interpretable model-agnostic explanations (LIME) were used to explain the decisions of the RF model. Different fat replacement patterns for BMD and LGMDR12 observed in the glutei, adductors and vasti as described in literature were in part confirmed by the explanations.
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Acknowledgments
We thank the patients and healthy volunteers for their participation in the study. KGC is Chairholder of the Emil von Behring Chair for Neuromuscular and Neurodegenerative Disorders by CSL Behring. KGC is member of the European Reference Network for Rare Neuromuscular Diseases (ERN EURO-NMD) and of the European Reference Network for Rare Neurological Diseases (ERN-RND). The authors report no disclosures relevant to the manuscript.
Funding
This work is supported in part by the Internal Funds KU Leuven under Grant C24/18/047 and by the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme. BDW is supported by the Fund for Scientific Research Flanders (FWO, PhD fellowship fundamental research grant number 1159121N).
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Huysmans, L., De Wel, B., Iterbeke, L., Claeys, K., Maes, F. (2024). Deep Learning Approaches for Automated Classification of Muscular Dystrophies from MRI. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_24
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DOI: https://doi.org/10.1007/978-981-97-1335-6_24
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