A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities.

Multi-atlas based segmentation techniques constitute an effective approach in the automatic segmentation of medical images. Existing methods usually rely on single spectral descriptors extracted from a specific imaging modality. In this paper, we propose the Multi-View Knee Cartilage Segmentation (MV-KCS) approach, for segmenting the knee joint articular cartilage from MR images. Operating under the Semi-supervised learning framework, MV-KCS leverages spectral content from multiple feature spaces by constructing sparse graphs for each view individually, and aggregating them via optimisation to obtain a common data graph. In We consider two multi-view scenarios: in the former case views correspond to multiple feature descriptors, while on the latter, the views correspond to multiple image modalities. We propose two effective labelling schemes, implementing label propagation from the atlas library to the target image. The proposed methodology is applied to the publicly available Osteoarthritis Initiative repository. We devise a comprehensive experimental design to validate different test cases, comparing single-feature vs multi-features, multi-features vs feature stacking and multi-features vs multi-modalities. Comparative results and statistical analysis reveal that the proposed MV-KCS provides enhanced performance ($DSC = 92.56\% \left({FC} \right), 89.91\% \left({TC} \right)$ DSC = 92.56 % FC , 89.91 % TC ), outperforming a series of patch-based approaches, six recent state-of-the-art deep supervised models and three deep semi-supervised ones, in terms of both classification and volumetric measures. [ABSTRACT FROM AUTHOR]