A cardiac magnetic resonance image segmentation method and apparatus, a terminal device and a computer-readable storage medium. Said method comprises: acquiring a cardiac magnetic resonance image to be segmented (S101); performing a first pre-processing operation on data of said cardiac magnetic resonance image (S102); inputting the pre-processed cardiac magnetic resonance image to be segmented into a pre-trained three-dimensional fully convolutional neural network model, so as to obtain a classification result (S103); and according to the classification result, obtaining a segmentation result of said cardiac magnetic resonance image (S104). The three-dimensional fully convolutional neural network model is a U-type fully convolutional network, comprising a contraction part and an expansion part corresponding to the contraction part, a dense block is connected before each convolution in the contraction part and the expansion part, the dense block is a DenseNet network having a preset number of layers, and a composite operation consisting of batch normalization, ReLu activation function and convolution is used for the connection between layers of the dense block. Said method is able to improve the segmentation accuracy.
CARDIAC MAGNETIC RESONANCE IMAGE SEGMENTATION METHOD AND APPARATUS, TERMINAL DEVICE AND STORAGE MEDIUM