Novel Uunsupervised Segmentation of Bone Marrow Edema-Like Lesions using Bayesian Conditional Generative Adversarial Networks

Quantitative assessment of the bone marrow edema-like lesions and their association with osteoarthritis requires a consistent and unbiased segmentation method, which is difficult to obtain in the presence of human annotators. This study proposes an unsupervised approach using Bayesian deep learning and conditional generative adversarial networks that detects and segments anomalies without human intervention. The full pipeline has a lesion-wide sensitivity of 0.86 on unseen scans. This approach is expected to be generalizable to other lesions and/or modalities.