Research on knee cartilage segmentation method based on deep learning

Dongsheng Ji; Lina Yang; Wenbo Zhang

doi:10.1117/12.3033571

13 June 2024 Research on knee cartilage segmentation method based on deep learning

Dongsheng Ji, Lina Yang, Wenbo Zhang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318072 (2024) https://doi.org/10.1117/12.3033571
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Knee osteoarthritis is a chronic disease that is common in the middle-aged and elderly population, mainly caused by damage or degeneration of the knee joint cartilage. Therefore, research on knee joint cartilage has significant practical significance. With the development of deep learning technology, AI-based automated segmentation methods have gradually become a research hotspot. However, the original U-Net model faces challenges in knee cartilage segmentation tasks such as weak feature extraction in the encoding phase, loss of contextual features in the decoding phase, and insufficient loss functions. To address these issues, a study proposed an improved U-Net method for knee joint cartilage segmentation. This method introduces an encoder fusion module in the encoding path and a context-selective kernel decode module in the decoding path to overcome potential challenges of feature loss and lack of contextual information in the original network. Experimental results demonstrate that the improved U-Net method exhibits significant advantages in knee osteoarthritis image segmentation tasks compared to other mainstream image segmentation algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongsheng Ji, Lina Yang, and Wenbo Zhang "Research on knee cartilage segmentation method based on deep learning", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318072 (13 June 2024); https://doi.org/10.1117/12.3033571

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KEYWORDS

Image segmentation

Cartilage

Feature extraction

Convolution

Image processing algorithms and systems

Magnetic resonance imaging

Deep learning

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