Temporal information enhancement-based method for 3D medical image segmentation

A temporal information enhancement-based method for 3D medical image segmentation, comprising:

• a training phase:
• performing semi-supervised training on a segmentation model by using a training set and a circle transformer module,
• the training set comprising a 3D medical image sequence comprising a target region, wherein part of the 3D medical image sequence is labeled data, and the rest is unlabeled data,
• the segmentation model comprising an encoder and a decoder, wherein the encoder is used to respectively encode each image in the 3D medical image sequence to acquire an encoded feature, and the circle transformer module is used to respectively perform processing on each image in the 3D medical image sequence as a target image to acquire a pre-decoded feature thereof, the processing comprising:
• performing self-attention calculation by using the encoded feature fs of the target image as K and V and by using the encoded feature fd of the other images in the 3D medical image sequence as Q, to acquire fm;
• and performing self-attention calculation again by using fm as K and V and by using fs as Q, to acquire fa, and performing layer normalization and feedforward neural network calculation on fa to acquire a pre-decoded feature fo of the target image, the decoder being used to respectively decode fo of each image to acquire a segmentation result of the image,
• for the labeled data, a training objective thereof being to minimize a difference between the segmentation result of each image and a label thereof, and for the unlabeled data, a training objective thereof being to minimize a difference between the segmentation result of each image and a segmentation result acquired by inputting the encoded feature of the image to the decoder, and
• an application phase:
• inputting a 3D medical image to be segmented to the trained segmentation model to acquire a segmentation result.