A multiple neural networks-based MRI image segmentation method and apparatus, and a device. Said method comprises the following steps: acquiring an original MRI image, pre-processing the original MRI image, and generating a processed MRI image; extracting an axial plane image block, a coronal plane image block and a sagittal plane image block from the processed MRI image; using the axial plane image block, the coronal plane image block and the sagittal plane image block respectively as inputs of a trained axial segmentation model, coronal segmentation model and sagittal segmentation model correspondingly, and obtaining segmentation results of the models; and fusing the segmentation results of the models, and obtaining a final segmentation result on the basis of a fusing result and a set volume constraint, wherein the axial segmentation model, the coronal segmentation model and the sagittal segmentation model are closely connected 2D-CNN neural network segmentation models with the same structure. Compared with the prior art, the method has the advantages of short operation time and high accuracy.
MULTIPLE NEURAL NETWORKS-BASED MRI IMAGE SEGMENTATION METHOD AND APPARATUS, AND DEVICE