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Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative

Please always quote using this URN: urn:nbn:de:0297-zib-71439
  • Volumetry of the cartilage of the knee, as needed for the assessment of knee osteoarthritis (KOA), is typically performed in a tedious and subjective process. We present an automated segmentation-based method for the quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data as well as cartilage volumetry readings given by clinical experts for 1378 subjects. It was shown that 3D CNNs can be employed for cartilage volumetry with an accuracy similar to expert volumetry readings. In future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as assessment of KOA progression via longitudinal analysis.

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Author:Alexander TackORCiD, Stefan ZachowORCiD
Document Type:ZIB-Report
Tag:Deep Learning; cartilage morphometry; imaging biomarker; radiomics; volume assessment
Date of first Publication:2019/01/24
Series (Serial Number):ZIB-Report (19-05)
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