Abstract #4346

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

Andrew Seohwan Yu^1,2,3, Sibaji Gaj^1,2, William Holden^1,2, Richard Lartey^1,2, Jeehun Kim^1,2,4, Carl Winalski^1,2,5, Naveen Subhas^1,2,5, and Xiaojuan Li^1,2,5

¹Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, ²Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States, ³Cleveland Clinic, Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, United States, ⁴Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States, ⁵Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, SegmentationQuantitative 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.

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