Full and weak supervision networks for meniscus segmentation and multi classification based on MRI: data from the Osteoarthritis Initiative

Quantitative MRI of meniscus morphology (such as MOAKS system) have shown clinical relevance in the diagnosis of osteoarthritis. However, it requires a large workload, and often lead to deviation due to reader’s subjectivity. Therefore, based on automated segmentation of six horns of meniscus using fully and weakly supervised networks, we established two-layer cascaded classification models that can detect the meniscal lesions and further classify them into three types, and finally achieved excellent performance. This can improve the efficiency and accuracy of using quantitative MRI to study KOA in the future.