Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms

Albert Swiecicki; Nicholas Said; Jonathan O'Donnell; Mateusz Buda; Nianyi Li; William A. Jiranek; Maciej A. Mazurowski

doi:10.1117/12.2551377

16 March 2020 Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms

Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, Mateusz Buda, Nianyi Li, William A. Jiranek, Maciej A. Mazurowski

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143N (2020) https://doi.org/10.1117/12.2551377
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Evaluating the severity of knee osteoarthritis (OA) accounts for significant plain film workload and is a crucial component of knee radiograph interpretation, which informs surgical decision-making for costly and invasive procedures such as knee replacement. The Kellgren-Lawrence (KL) grading scale systematically and quantitatively assesses the severity of knee OA but is associated with notable inter-reader variability. In this study, we propose a deep learning method for the assessment of joint space narrowing (JSN) in the knee, which is an essential part of determining the KL grade. To determine the extent of JSN, we analyzed 99 knee radiographs to calculate the distance between the femur and tibia. Our algorithm's measurements of JSN and KL grade correlated well other radiologists' assessments. The average distance (in pixels) between the femur and tibia bones as measured by our algorithm was 9.60 for KL=0, 7.60 for KL=1, 6.89 for KL=2, 3.75 for KL=3, 1.25 for KL=4. Additionally, we used 100 manually annotated knee radiographs to train the algorithm to segment the femur and tibia bones. When evaluated on an independent set of 20 knee radiographs, the algorithm demonstrated a Dice coefficient of 96.59%. An algorithm for measurement of JSN and KL grades may play a significant role in automatically, reliably, and passively evaluating knee OA severity and influence and surgical decision-making and treatment pathways.

Citation Download Citation

Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, Mateusz Buda, Nianyi Li, William A. Jiranek, and Maciej A. Mazurowski "Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143N (16 March 2020); https://doi.org/10.1117/12.2551377

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