Region‐based two‐stage MRI bone tissue segmentation of the knee joint

In medical image segmentation, the neural network structure of the U‐Net family has demonstrated sufficient advantages. However, MRI images have different scan parameters and different scan times, resulting in different feature representation of the images. Furthermore, there is a great class imbalance between bone and cartilage tissues in MRI knee images. To address these issues, a region‐based two‐stage MRI knee bone tissue segmentation network is proposed in this paper. The segmentation network makes full use of the location characteristics of the three types of bone tissue in the knee joint and uses a two‐stage network architecture with a modified U2‐Net backbone network to segment MRI knee bone tissue. The neural network structure is divided into two phases, the first phase with a simple coded decoding structure for saliency detection to obtain the positional regional relationships of different bone tissues, and the second phase with a segmentation network consisting of 2 modified U2‐Net, one for segmenting the patella and associated cartilage and the other for segmenting the femur, tibia and associated cartilage. The algorithm was tested with a variety of MRI knee data to verify the effectiveness of the algorithm.