Automatic segmentation for meniscus magnetic resonance images of knee joint based on Mask region-based convolution neural network

Over the past two decades, magnetic resonance imaging (MRI) has been widely applied into the diagnosis of knee joint diseases. Due to the complexity and diversity of MRI data, traditional feature extraction requires manual searching for features to segment meniscus, and the final segmentation results still need to be further filtered. Therefore, it is necessary to design a novel method to automatically extract features directly from images. In this study, we develop a framework to implement this goal by using a mask region-based convolution neural network (Mask R-CNN) without manual intervention. In order to highlight the proportion of meniscus, we first preprocess the original image data so that it is reduced to about 1/8 of the original size, and then input the preprocessed image data into the trained Mask R-CNN. Afterwards, transfer learning is used to generate the weight of our network. By testing 1000 images, the mean intersection over union (IOU) and dice similarity coefficient (DSC) are up to 83.68% and 91.13%, respectively. The current results demonstrate that our approach is feasible and has a potential significance in clinical practice.