Abstract
Knee arthritis is one of the most common chronic degenerative joint diseases in the world, affecting the quality of life of a considerable part of the Modern population. Therefore, the early detection of knee arthritis is of great significance for diagnosis and treatment. Magnetic resonance imaging (MRI) is one of the most commonly used methods for evaluating joint degeneration in osteoarthritis research. In order to obtain information on knee cartilage degradation from MRI, it is necessary to segment the articular cartilage interface and cartilage surface boundary on the entire joint surface. In this work, we propose a novel cascaded network structure with an effective inception-like multi-scale module for knee joint magnetic resonance images segmentation. Compared with the baseline, a maximum of 1.6% dice score mean promotion is obtained. The code is publicly available at https://github.com/ETVP
Supported by Shanghai BNC.
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Liu, J., Hua, C., Zhang, L., Li, P., Lu, X. (2021). Knee Cartilages Segmentation Based on Multi-scale Cascaded Neural Networks. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_3
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