A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning

Implementation and evaluation methods

All experiments used the Pytorch framework and were implemented on the RTX 3060 GPU. The batch size was 4, and each image was resized to 320 \(\times\) 320 and normalized by mean and standard deviation. We used an Adam as the network optimizer and set the initial learning rate to 0.001. Additionally, we used a cosine annealing learning rate scheduler with a minimum learning rate as high as 0.00001. Horizontal flipping, vertical flipping, and random cropping were used to augment the data. We used a joint loss function of cross entropy (BCE) and dice loss…