Research on an improved few-shot medical image segmentation algorithm
In view of the small sample characteristics of medical images and the poor generalization ability of segmentation models,the paper proposes a medical image segmentation network based on meta-learning.Firstly,based on the 3D U-Net network,the down-sampling module is increased from two layers of 3D convolutional layers to three layers,and the batch normalization of each layer of 3D convolutional layers is improved to group normalization.Secondly,at the connection of the U-shaped network codec,a Transformer module is introduced to enhance the model's ability to extract global information.An improved attention gate mechanism is introduced at the U-shaped network skip connection,which replaces batch normalization with group normalization,optimizes the effect of using low batches to train the model,and replaces Softmax activation function with ReLU activation function.Finally,the model is trained using Model-Agnostic Meta-Learning(MAML)algorithm.The experimental results on the public dataset M&Ms show that the Dice score and Hausdorff distance are 69.9%and 11.88 mm.Compared with other mainstream algorithms,the model segmentation accuracy and generalization ability is better.
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