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Abstract #0201

Automated Knee Cartilage Segmentation with Very Limited Training Data: Combining Convolutional Neural Networks with Transfer Learning

Alexander R Toews1,2, Zhongnan Fan3, Marianne S Black2,4, Jin Hyung Lee1,3,5,6,7, Garry E Gold2,5,8, Brian A Hargreaves1,2,5, and Akshay S Chaudhari2,5

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3LVIS Corporation, Palo Alto, CA, United States, 4Mechanical Engineering, Stanford University, Stanford, CA, United States, 5Bioengineering, Stanford University, Stanford, CA, United States, 6Neurology & Neurological Sciences, Stanford University, Stanford, CA, United States, 7Neurosurgery, Stanford University, Stanford, CA, United States, 8Orthopaedic Surgery, Stanford University, Stanford, CA, United States

Magnetic resonance imaging is commonly used to study osteoarthritis. In most cases, manual cartilage segmentation is required. Recent advances in deep-learning methods have shown promise for automating cartilage segmentation, but they rely on the availability of large training datasets that rarely represent the exact nature or extent of data practically available in routine research studies. The goal of this study was to automate cartilage segmentation in studies with very few training datasets available by creating baseline segmentation knowledge from larger training datasets, followed by creating transfer learning models to adapt this knowledge to the limited datasets utilized in typical study.

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