Pelvic segmentation based MultiR2UNet

For MRI images of pelvis, it is helpful for doctors to extract the structure of pelvis quickly and accurately. Then the disease in the pelvic area can be diagnosed and analyzed in time. Extracting skeletal contour from MRI images of pelvis is not only time consuming but also low precice. Therefore, this paper proposes an improved image segmentation algorithm based on MultiR2UNet. We adopted R2UNet, which is more accurate in the segmentation field, as the backbone network. The residual connection is used in the network hopping layer, and the MultiRes Block is used in the up-sampling, which is beneficial to increase the depth of the network and to extract more detailed features. Due to the small number of pelvis training samples and the imbalance of samples, we performed data enhancement in the data preprocessing stage. The data samples were effectively amplified. In the training phase, we propose to use the mixed loss function. After several times of training and detection, the gap between the pelvis section segmentation by the algorithm in this paper and the real label is fairly small, and their coincidence degree can reach about 91%. The average segmentation time for each image was about 0.012s. The experimental results show that the proposed algorithm can guarantee the segmentation accuracy. MultiR2UNet is an effective real-time pelvis segmentation algorithm.