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3 April 2024 Inpainting MRI for unsupervised knee bone marrow edema-like lesion segmentation using conditional diffusion models
Andrew Seohwan Yu, Richard Lartey, William Holden, Ahmet Hakan Ok, Jeehun Kim, Carl Winalski, Naveen Subhas, Vipin Chaudhary, Xiaojuan Li
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Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310O (2024) https://doi.org/10.1117/12.3008575
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
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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Andrew Seohwan Yu, Richard Lartey, William Holden, Ahmet Hakan Ok, Jeehun Kim, Carl Winalski, Naveen Subhas, Vipin Chaudhary, and Xiaojuan Li "Inpainting MRI for unsupervised knee bone marrow edema-like lesion segmentation using conditional diffusion models", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310O (3 April 2024); https://doi.org/10.1117/12.3008575
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KEYWORDS
Bone
Diffusion
Image segmentation
Magnetic resonance imaging
Education and training
Medical imaging
Convolutional neural networks
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