A Novel Deep Learning Model for Knee Cartilage 3D Segmentation

Over the past few years, osteoarthritis is one of the most common knee diseases. The diagnosis and treatment of this disease is a vital importance. Furthermore, Deep Learning (DL) approaches have shown a good performance in learning the high-level features and in resolving segmentation issues. In this paper, we propose a new model of DL 3D-Res-UNet in order to accurately segment the knee cartilage from a Magnetic Resonance Imaging (MRI) 3D construction that allows perfectly visualizing the anatomical structure of this organ. This model is a combination of an architecture widely known in the literature for its segmentation efficiency (3D-UNet) with a residual architecture (3D-ResNet). The experimental results reveal that the suggested model can stabilize the training model, segment the cartilage from MRI very well and compete with the state of art on multiple performance metrics. The gain of precision and recall with 3DRes-UNet is given up to 15% and 26% compared to 3D-ResNet and 3D-UNet, respectively, hence the effectiveness of the suggested method.