Lesion segmentation in medical images, particularly for Bone Marrow Edema-like Lesions (BMEL) in the knee, faces challenges due to imbalanced data and unreliable annotations. This study proposes an unsupervised deep learning method with the use of conditional diffusion models coupled with inpainting tasks for anomaly detection. This innovative approach facilitates the detection and segmentation of BMEL without human intervention, achieving a DICE testing score of 0.2223. BMEL has been shown to correlate and predict disease progression in several musculoskeletal disorders, such as osteoarthritis. With further development, our method has great potential for fully automated analysis of BMEL to improve early diagnosis and prognosis for musculoskeletal disorders. The framework can be extended to other lesion detection as well.
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