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Abstract #1137

Automatic Segmentation of Knee Cartilage and Menisci from MRI Data: Efficient Multiclass Solution Based on Deep Learning

Egor Panfilov1, Aleksei Tiulpin1, Miika Nieminen1,2, and Simo Saarakkala1,2

1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland, 2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland

Manual segmentation of articular cartilage and menisci from magnetic resonance data is time-consuming and can be challenging. Recently, deep learning has shown promising results in medical image segmentation. The aim of this study was to develop a method for automatic segmentation of articular cartilage and menisci that performs more accurately and efficiently than the previously published methods. On OAI/iMorphics dataset the method achieves Dice score of 90.7±1.9 for femoral cartilage, 89.7±2.8 for tibial, 87.1±4.7 for patellar, and 86.3±3.4 for menisci. The presented results could facilitate the osteoarthritis research and enhance clinical practice. Source codes and pretrained models will be open-sourced.

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